 Research
 Open Access
 Published:
A novel method for inference of acyclic chemical compounds with bounded branchheight based on artificial neural networks and integer programming
Algorithms for Molecular Biology volume 16, Article number: 18 (2021)
Abstract
Analysis of chemical graphs is becoming a major research topic in computational molecular biology due to its potential applications to drug design. One of the major approaches in such a study is inverse quantitative structure activity/property relationship (inverse QSAR/QSPR) analysis, which is to infer chemical structures from given chemical activities/properties. Recently, a novel twophase framework has been proposed for inverse QSAR/QSPR, where in the first phase an artificial neural network (ANN) is used to construct a prediction function. In the second phase, a mixed integer linear program (MILP) formulated on the trained ANN and a graph search algorithm are used to infer desired chemical structures. The framework has been applied to the case of chemical compounds with cycle index up to 2 so far. The computational results conducted on instances with n nonhydrogen atoms show that a feature vector can be inferred by solving an MILP for up to \(n=40\), whereas graphs can be enumerated for up to \(n=15\). When applied to the case of chemical acyclic graphs, the maximum computable diameter of a chemical structure was up to 8. In this paper, we introduce a new characterization of graph structure, called “branchheight” based on which a new MILP formulation and a new graph search algorithm are designed for chemical acyclic graphs. The results of computational experiments using such chemical properties as octanol/water partition coefficient, boiling point and heat of combustion suggest that the proposed method can infer chemical acyclic graphs with around \(n=50\) and diameter 30.
Background
In computational molecular biology, various types of data have been utilized, which include sequences, gene expression patterns, and protein structures. Graph structured data have also been extensively utilized, which include metabolic pathways, proteinprotein interaction networks, gene regulatory networks, and chemical graphs. Much attention has recently been paid to the analysis of chemical graphs due to its potential applications to computeraided drug design. One of the major approaches to computeraided drug design is quantitative structure activity/property relationship (QSAR/QSPR) analysis, the purpose of which is to derive quantitative relationships between chemical structures and their activities/properties. Furthermore, inverse QSAR/QSPR has been extensively studied [1, 2], the purpose of which is to infer chemical structures from given chemical activities/properties. Inverse QSAR/QSPR is often formulated as an optimization problem to find a chemical structure maximizing (or minimizing) an objective function under various constraints.
In both QSAR/QSPR and inverse QSAR/QSPR, chemical compounds are usually represented as vectors of real or integer numbers, which are often called descriptors and correspond to feature vectors in machine learning. Using these chemical descriptors, various heuristic and statistical methods have been developed for finding optimal or nearly optimal graph structures under given objective functions [1, 3, 4]. Inference or enumeration of graph structures from a given feature vector is a crucial subtask in many of such methods. Various methods have been developed for this enumeration problem [5,6,7,8] and the computational complexity of the inference problem has been analyzed [9, 10]. On the other hand, enumeration in itself is a challenging task, since the number of molecules (i.e., chemical graphs) with up to 30 atoms (vertices) C, N, O, and S, may exceed \(10^{60}\) [11].
As a new approach, artificial neural network (ANN) and deep learning technologies have recently been applied to inverse QSAR/QSPR. For example, variational autoencoders [12], recurrent neural networks [13, 14], and grammar variational autoencoders [15] have been applied. In these approaches, new chemical graphs are generated by solving a kind of inverse problems on neural networks that are trained using known chemical compound/activity pairs. However, the optimality of the solution is not necessarily guaranteed in these approaches. In order to guarantee the optimality mathematically, a novel approach has been proposed [16] for ANNs, using mixed integer linear programming (MILP).
Recently, a new framework has been proposed [17,18,19] by combining two previous approaches: efficient enumeration of treelike graphs [5], and MILPbased formulation of the inverse problem on ANNs [16]. This combined framework for inverse QSAR/QSPR mainly consists of two phases. The first phase solves (I) Prediction Problem, where a feature vector f(G) of a chemical graph G is introduced and a prediction function \(\psi _{{{\mathcal {N}}}}\) on a chemical property \(\pi \) is constructed with an ANN \({{\mathcal {N}}}\) using a data set of chemical compounds G and their values a(G) of \(\pi \). The second phase solves (II) Inverse Problem, where (IIa) given a target value \(y^*\) of the chemical property \(\pi \), a feature vector \(x^*\) is inferred from the trained ANN \({{\mathcal {N}}}\) so that \(\psi _{{{\mathcal {N}}}}(x^*)\) is close to \(y^*\) and (IIb) then a set of chemical structures \(G^*\) such that \(f(G^*)= x^*\) is enumerated by a graph search algorithm. In (IIa) of the abovementioned previous methods [17,18,19], an MILP is formulated for acyclic chemical compounds. Afterwards, Ito et al. [20] and Zhu et al. [21] designed a method of inferring chemical graphs with cycle index 1 and 2, respectively, by formulating a new MILP and using an efficient algorithm for enumerating chemical graphs with cycle index 1 [22] and cycle index 2 [23, 24]. The computational results conducted on instances with n nonhydrogen atoms show that a feature vector \(x^*\) can be inferred for up to around \(n=40\) whereas graphs \(G^*\) can be enumerated for up to around \(n=15\).
In this paper, we present a new characterization of graph structure, called “branchheight.” Based on this, we can treat a class of acyclic chemical graphs with a structure that is topologically restricted but frequently appears in a chemical database, formulate a new MILP formulation that can handle acyclic graphs with a large diameter, and design a new graph search algorithm that generates acyclic chemical graphs with up to around 50 vertices. The results of computational experiments using such chemical properties as octanol/water partition coefficient, boiling point and heat of combustion suggest that the proposed method is much more useful than the previous method.
The paper is organized as follows. "Preliminary" section introduces some notions on graphs, a modeling of chemical compounds and a choice of descriptors. "A method for inferring chemical graphs" section reviews the framework for inferring chemical compounds based on ANNs and MILPs. "MILPs for chemical acyclic graphs with bounded branchheight" section introduces a new method of modeling acyclic chemical graphs and proposes a new MILP formulation that represents an acyclic chemical graph G with n vertices, where our MILP requires only O(n) variables and constraints when the branchparameter k and the kbranch height in G (graph topological parameters newly introduced in this paper) is constant. "A new graph search algorithm" section describes the idea of our new dynamic programming type of algorithm that enumerates a given number of acyclic chemical graphs for a given feature vector. "Experimental results" section reports the results on some computational experiments conducted for chemical properties such as octanol/water partition coefficient, boiling point and heat of combustion. "Concluding remarks" section makes some concluding remarks. Appendix A provides the statistical distribution of structural features of acyclic chemical graphs in a chemical graph database. Appendices B and C describe the idea of our MILP formulation and the details of all variables and constraints in the MILP formulation, respectively. Appendix D presents descriptions of our new graph search algorithm.
Preliminary
This section introduces some notions and terminology on graphs, a modeling of chemical compounds and our choice of descriptors.
Let \({\mathbb {R}}\), \({\mathbb {Z}}\) and \({\mathbb {Z}}_+\) denote the sets of reals, integers and nonnegative integers, respectively. For two integers a and b, let [a, b] denote the set of integers i with \(a\le i\le b\).
Graphs
A graph stands for a simple undirected graph, where an edge joining two vertices u and v is denoted by uv \((= vu)\). The sets of vertices and edges of a graph H are denoted by V(H) and E(H), respectively. Let \(H=(V,E)\) be a graph with a set V of vertices and a set E of edges. For a vertex \(v\in V\), the set of neighbors of v in H is denoted by \(N_H(v)\), and the degree \(\deg _H(v)\) of v is defined to be \(N_H(v)\). The length of a path is defined to be the number of edges in the path. The distance \({\text {dist}}_H(u,v)\) between two vertices \(u,v\in V\) is defined to be the minimum length of a path connecting u and v in H. The diameter \({\text {dia}}(H)\) of H is defined to be the maximum distance between two vertices in H; i.e., \({\text {dia}}(H)\triangleq \max _{u,v\in V}{\text {dist}}_H(u,v)\). Denote by \(\ell (P)\) the length of a path P.
Centers of trees For a tree T with an even (resp., odd) diameter d, the center is defined to be the vertex v (resp., the adjacent vertex pair \(\{v,v'\}\)) that situates in the middle of one of the longest paths, with length d. The center of each tree is uniquely determined.
Rooted trees A rooted tree is defined to be a tree where a vertex (or a pair of adjacent vertices) is designated as the root. Let T be a rooted tree, where for two adjacent vertices u and v, vertex u is called the parent of v if u is closer to the root than v is. The height \({\text {height}}(v)\) of a vertex v in T is defined to be the maximum length of a path from v to a leaf u in the descendants of v, where \({\text {height}}(v)=0\) for each leaf v in T. Figure 1a and b illustrate examples of trees rooted at the center.
Degreebounded trees For positive integers a, b and c with \(b\ge 2\), let T(a, b, c) denote the rooted tree such that the number of children of the root is a, the number of children of each nonroot internal vertex is b and the distance from the root to each leaf is c. We see that the number of vertices in T(a, b, c) is \(a(b^c1)/(b1)+1\), and the number of nonleaf vertices in T(a, b, c) is \(a(b^{c1}1)/(b1)+1\). In the rooted tree T(a, b, c), we denote the vertices by \(v_1,v_2,\ldots ,v_n\) with a breadthfirstsearch order, and denote the edge between a vertex \(v_i\) with \(i\in [2,n]\) and its parent by \(e_i\), where \(n=a(b^c1)/(b1)+1\) and each vertex \(v_i\) with \(i\in [1, a(b^{c1}1)/(b1)+1]\) is a nonleaf vertex. For each vertex \(v_i\) in T(a, b, c), let \(\text {Cld}(i)\) denote the set of indices j such that \(v_j\) is a child of \(v_i\), and \(\text {prt}(i)\) denote the index j such that \(v_j\) is the parent of \(v_i\) when \(i\in [2,n]\). Let \(P_{\text {prc}}(a,b,c)\) be a set of ordered index pairs (i, j) of vertices \(v_i\) and \(v_j\) in T(a, b, c). We call \(P_{\text {prc}}(a,b,c)\) proper if the next conditions hold:

(a)
For each pair of vertices \(v_i\) and \(v_j\) in T(a, b, c) such that \(v_i\) is the parent of \(v_j\), there is a sequence \((i_1,i_2),(i_2,i_3),\ldots ,(i_{k1},i_k)\) of index pairs in \(P_{\text {prc}}(a,b,c)\) such that \(i_1=i\) and \(i_k=j\); and

(b)
Each subtree \(H=(V,E)\) of T(a, b, c) with \(v_1\in V\) is isomorphic to a subtree \(H'=(V',E')\) by a graph isomorphism \(\psi :V\rightarrow V'\) with \(\psi (v_1)=v_1\) so that if \(v_j\in V'\) for a pair \((i,j)\in P_{\text {prc}}(a,b,c)\) then \(v_i\in V'\).
Note that a proper set \(P_{\text {prc}}(a,b,c)\) is not necessarily unique.
Branchheight in trees In this paper, we introduce “branchheight” of a tree as a new measure to the “agglomeration degree” of trees. We specify a nonnegative integer k, called a branchparameter to define branchheight. First we regard T as a rooted tree by choosing the center of T as the root. Figure 1a, b illustrate examples of rooted trees. We introduce the following terminology on a rooted tree T.

A leaf kbranch: A nonroot vertex v in T such that \({\text {height}}(v)= k\).

A nonleaf kbranch: A nonroot vertex v in T such that v has at least two children, and for each child u of v it holds that \({\text {height}}(u)\ge k\). We call a leaf or a nonleaf kbranch a kbranch. Figure 2a–c illustrate the kbranches of the rooted tree \(H_2\) in Fig. 1b for \(k=1,2\) and 3, respectively.

A kbranchpath: A path P in T that joins two vertices u and \(u'\) such that each of u and \(u'\) is the root or a kbranch and P does not contain the root or a kbranch as an internal vertex.

The kbranchsubtree of T: The subtree of T that consists of the edges in all kbranchpaths of T. We call a vertex (resp., an edge) in T a kinternal vertex (resp., a kinternal edge) if it is contained in the kbranchsubtree of T and a kexternal vertex (resp., a kexternal edge) otherwise. Let \(V^{\text {in}}\) and \(V^\text {ex}\) (resp., \(E^{\text {in}}\) and \(E^\text {ex}\)) denote the sets of kinternal and kexternal vertices (resp., edges) in T.

The kbranchtree of T: The rooted tree obtained from the kbranchsubtree of T by replacing each kbranchpath with a single edge. Figure 1c illustrates the 2branchtree of the rooted tree \(H_2\) in Fig. 1b. Notice that by our definitions, leaf kbranches and nonleaf kbranches are leaves and branching points in the kbranchtree.

A kfringetree: One of the connected components that consists of the edges not in the kbranchsubtree. Each kfringetree \(T'\) contains exactly one vertex v in the kbranchsubtree, where \(T'\) is regarded as a tree rooted at v. Note that the height of any kfringetree is at most k. Figure 2a–c illustrate the kfringetrees of the rooted tree \(H_2\) in Fig. 1b for \(k=1, 2\) and 3, respectively.

The kbranchleaf number \({\text {bl}}_k(T)\): The number of leaf kbranches in T. For the trees \(H_i\), \(i=1,2\) in Fig. 1a, b, it holds that \({\text {bl}}_0(H_1)= {\text {bl}}_0(H_2)=8\), \({\text {bl}}_1(H_1)= {\text {bl}}_1(H_2)=5\), \({\text {bl}}_2(H_1)= {\text {bl}}_2(H_2)=3\) and \({\text {bl}}_3(H_1)= {\text {bl}}_3(H_2)=2\).

The kbranch height \(\text {bh}_k(T)\) of T: The maximum number of kbranches along a path from the root to a leaf of T; i.e., \(\text {bh}_k(T)\) is the height of the kbranchtree \(T^*\) (the maximum length of a path from the root to a leaf in \(T^*\)). For the example of trees \(H_i\), \(i=1,2\) in Fig. 1a, b, it holds that \(\text {bh}_0(H_1)=\text {bh}_0(H_2)=3\), \(\text {bh}_1(H_1)=\text {bh}_1(H_2)=3\), \(\text {bh}_2(H_1)=\text {bh}_2(H_2)=2\) and \(\text {bh}_3(H_1)=\text {bh}_3(H_2)=1\).
Even though this paper deals exclusively with acyclic graphs, we formally introduce the kbranch height for chemical cyclic graphs (chemical graphs that contain at least one cycle). The core of a chemical cyclic graph G is defined to be the induced subgraph \(G'\) of G that consists of vertices in a cycle or the vertices in a path joining two cycles. A vertex in the core (not in the core) is called a core vertex (resp., a noncore vertex). The edges not in the core of a chemical cyclic graph G form a collection of trees T, which we call a noncore tree. Each noncore tree contains exactly one core vertex and is regarded as a tree rooted at the core vertex. The kbranch height of a chemical cyclic graph G is defined to be the maximum of kbranch heights over all noncore trees. We observe that most chemical graphs G with at most 50 nonhydrogen atoms satisfy \(\text {bh}_2(G)\le 2\). See Appendix A for a summary of statistical feature distribution of chemical graphs registered in the chemical database PubChem [25].
For convenient reference, we summarize the graphrelated notation used throughout this paper in Table 1.
Modeling of chemical compounds
We represent the graph structure of a chemical compound as a graph with labels on vertices and multiplicity on edges in a hydrogensuppressed model. Let \(\Lambda \) be a set of labels each of which represents a chemical element such as C (carbon), O (oxygen), N (nitrogen) and so on, where we assume that \(\Lambda \) does not contain H (hydrogen). Let \(\text {mass}({\mathtt{a}})\) and \({\text {val}}({\mathtt{a}})\) denote the mass and valence of a chemical element \({\mathtt{a}}\in \Lambda \), respectively. In our model, we use integer \(\text {mass}^*({\mathtt{a}})=\lfloor 10\cdot \text {mass}({\mathtt{a}})\rfloor \), \({\mathtt{a}}\in \Lambda \), and assume that each chemical element \({\mathtt{a}}\in \Lambda \) has a unique valence \({\text {val}}({\mathtt{a}})\in [1, 4]\).
We introduce a total order < over the elements in \(\Lambda \) according to their mass values; i.e., we write \(\mathtt{a<b}\) for chemical elements \(\mathtt{a,b}\in \Lambda \) with \(\text {mass}({\mathtt{a}})<\text {mass}(\mathtt{b})\). A pair of two atoms \({\mathtt{a}}\) and \(\mathtt{b}\), \(\mathtt{a, b} \in \Lambda \), joined with a bondmultiplicity \(m \in [1, 3]\), where \(m=1, 2, 3,\) correspond to single, double, and triple bonds, respectively, is denoted by a tuple \(\gamma =(\mathtt{a, b}, m)\), called the adjacencyconfiguration of the atom pair. Choose a set \(\Gamma _{<}\) of tuples \(\gamma =(\mathtt{a,b},m)\in \Lambda \times \Lambda \times [1,3]\) such that \(\mathtt{a<b}\). For a tuple \(\gamma =(\mathtt{a,b},m)\in \Lambda \times \Lambda \times [1,3]\), let \(\overline{\gamma }\) denote the tuple \((\mathtt{b,a},m)\). Set \(\Gamma _{>}=\{\overline{\gamma }\mid \gamma \in \Gamma _{<}\}\) and \(\Gamma _{=}=\{(\mathtt{a,a},m)\mid {\mathtt{a}}\in \Lambda , m\in [1,3]\}\), and \(\Gamma = \Gamma _{<}\cup \Gamma _{=}\).
We use a hydrogensuppressed model because hydrogen atoms can be added at the final stage.
Let \((H,\alpha ,\beta )\) be a tuple of a graph \(H=(V,E)\), a function \(\alpha :V\rightarrow \Lambda \) and a function \(\beta : E\rightarrow [1,3]\), where \(\alpha (v)={\mathtt{a}}\) and \(\beta (e)=m\) mean that a chemical element \({\mathtt{a}}\) is assigned to a vertex v and a bondmultiplicity m is assigned to an edge e, respectively. For a notational convenience, we denote the sum of bondmultiplicities of edges incident to a vertex \(u \in V\) by

\(\beta (u) \triangleq \sum _{uv \in E}\beta (uv)\).
A tuple \(G=(H,\alpha ,\beta )\) is called a chemical graph over \(\Lambda \) and \(\Gamma _{<}\cup \Gamma _{=}\) if the following holds:

(i)
H is connected;

(ii)
\((\alpha (u),\alpha (v),\beta (uv))\in \Gamma _{<}\cup \Gamma _{=}\) for each edge \(uv\in E\); and

(iii)
\(\beta (u) \le {\text {val}}(\alpha (u))\) for each vertex \(u\in V\).
A chemical graph \(G=(H,\alpha ,\beta )\) is called a “chemical acyclic graph” if the graph H is an acyclic graph. Similarly for other types of graphs for H.
We define the bondconfiguration of an edge \(e=uv \in E\) in a chemical graph G to be a tuple \((\deg _H(u),\deg _H(v),\beta (e))\) such that \(\deg _H(u)\le \deg _H(v)\) for the endvertices u and v of e. Let \(\text {Bc}\) denote the set of bondconfigurations \(\mu =(d_1,d_2,m)\in [1,4]\times [1,4]\times [1,3]\) such that \(\max \{d_1,d_2\}+m \le 5\). We regard that \((d_1,d_2,m)=(d_2,d_1,m)\).
In summary, we give the notation on modeling chemical compounds used throughout this paper in Table 2.
Descriptors
In our method, we use only graphtheoretical descriptors for defining a feature vector, which facilitates our design of an algorithm for constructing graphs. Given a chemical acyclic graph \(G=(H,\alpha ,\beta )\), we define a feature vector f(G) that consists of the following 11 kinds of descriptors. We choose an integer \(k^*\in [1,4]\) as a branchparameter.
General chemical graph descriptors

n(G): the number V of vertices.

\(\overline{\text {dia}}(G)\triangleq \text {dia}(H)/n(G)\): the diameter of H divided by \(n(G)=V\).

\(\overline{\text {ms}}\triangleq \sum _{v\in V}\text {mass}^*(\alpha (v))/n(G)\): the average \(\hbox {mass}^*\) of atoms in G.

\(n_\mathtt{H}(G)\): the number of hydrogen atoms to be added to G.
Descriptors for vertices of certain degree

\(\text {dg}_i^\text {t}(G)\triangleq \{v\in V^\text {t}\mid \deg _{H}(v)=i\},\) \(i\in [1,4],\) \(\text {t}\in \{{\text {in}},\text {ex}\}\): the number of \(k^*\)internal/\(k^*\)external vertices of degree i in H, where the bondmultiplicity of edges incident to a vertex v is ignored in the degree of v.
Descriptors for branchleaf number and branchheight

\({\text {bl}}_{k^*}(G)\): the \(k^*\)branchleaf number of G.

\(\text {bh}_{k^*}(G)\): the \(k^*\)branch height of G.
Descriptors for vertex labels

\(\text {ce}_{\mathtt{a}}^\text {t}(G)\triangleq \{ v\in V^\text {t}\mid \alpha (v)={\mathtt{a}}\},\) \({\mathtt{a}}\in \Lambda,\) \(\text {t}\in \{{\text {in}},\text {ex}\}\): the number of \(k^*\)internal/\(k^*\)external vertices with chemical element \({\mathtt{a}}\in \Lambda \).
Descriptors for the number of bonds

\(\text {bd}_m^\text {t}(G)\triangleq \{e\in E^\text {t}\mid \beta (e)=m\}\), \(m=2, 3\), \(\text {t}\in \{{\text {in}},\text {ex}\}\): the number of \(k^*\)internal/\(k^*\)external edges with bondmultiplicity m.
Descriptors for adjacencyconfigurations

\(\text {ac}_{\gamma }^\text {t}(G)\), \(\gamma \in \Gamma \), \(\text {t}\in \{{\text {in}},\text {ex}\}\): the number of \(k^*\)internal/\(k^*\)external edges \(e=uv\) with adjacencyconfiguration \(\gamma =(\mathtt{a,b},m)\) (i.e., \(\alpha (u)={\mathtt{a}},\alpha (v)=\mathtt{b}\) and \(\beta (e)=m\)) in G.
Descriptors for bondconfigurations

\(\text {bc}_{\mu }^\text {t}(G)\), \(\mu \in \text {Bc}\), \(\text {t}\in \{{\text {in}},\text {ex}\}\): the number of \(k^*\)internal/\(k^*\)external edges \(e=uv\) with bondconfiguration \(\mu =(d,d',m)\) (i.e., \(\deg _H(u)=d, \deg _H(v)=d'\) and \(\beta (e)=m\)) in G.
Note that
The number K of descriptors in our feature vector \(x=f(G)\) is \(K=2\Lambda +2\Gamma +50\). Note that the above K descriptors are not independent in the sense that some descriptors depend on the combination of other descriptors. For example, descriptor \(\text {bd}_i^{\text {in}}(G)\) can be determined by \(\sum _{\gamma =(\mathtt{a,b},m)\in \Gamma : m=i }\text {ac}_{\gamma }^{\text {in}}(G)\).
A method for inferring chemical graphs
Framework for the Inverse QSAR/QSPR
We review the framework that solves the inverse QSAR/QSPR by using MILPs [20, 21], which is illustrated in Fig. 3. For a specified chemical property \(\pi \) such as boiling point, we denote by a(G) the observed value of the property \(\pi \) for a chemical compound G. As the first phase, we solve (I) Prediction Problem with the following three steps.
Phase 1.
Stage 1: Let \(\text {DB}\) be a set of chemical graphs. For a specified chemical property \(\pi \), choose a class \({\mathcal {G}}\) of graphs such as acyclic graphs or monocyclic graphs. Prepare a data set \(D_{\pi }=\{G_i\mid i=1,2,\ldots ,m\}\subseteq {\mathcal {G}}\cap \text {DB}\) such that the value \(a(G_i)\) of each chemical graph \(G_i\), \(i=1,2,\ldots ,m\) is available. Set reals \({\underline{a}}, {\overline{a}}\in {\mathbb {R}}\) so that \({\underline{a}}\le a(G_i)\le {\overline{a}}\), \(i=1,2,\ldots ,m\).
Stage 2: Introduce a feature function \(f: {\mathcal {G}}\rightarrow {\mathbb {R}}^K\) for a positive integer K. We call f(G) the feature vector of \(G\in {\mathcal {G}}\), and call each entry of a vector f(G) a descriptor of G.
Stage 3: Construct a prediction function \(\psi _{{\mathcal {N}}}\) with an ANN \({{\mathcal {N}}}\) that, given a vector in \({\mathbb {R}}^K\), returns a real number in the range \([{\underline{a}},{\overline{a}}]\) so that \(\psi _{{\mathcal {N}}}(f(G))\) takes a value nearly equal to a(G) for many chemical graphs in \(\text {DB}\). See Fig. 3a–c for an illustration of Stages 1, 2, and 3 in Phase 1.
In this paper, we use the rangebased method to define an applicability domain (AD) [26] to our inverse QSAR/QSPR. Set \(\underline{x_j}\) and \(\overline{x_j}\) to be the minimum and maximum values of the jth descriptor \(x_j\) in \(f(G_i)\), respectively, over all graphs \(G_i\), \(i=1,2,\ldots ,m\), where we possibly normalize some descriptors such as \(\text {ce}_{\mathtt{a}}^{\text {in}}(G)\), which is normalized with \(\text {ce}_{\mathtt{a}}^{\text {in}}(G)/n(G)\). Define our AD \({\mathcal {D}}\) to be the set of vectors \(x\in {\mathbb {R}}^K\) such that \(\underline{x_j}\le x_j\le \overline{x_j}\) for the variable \(x_j\) of each jth descriptor, \(j=1,2,\ldots ,k\).
In the second phase, we try to find a vector \(x^*\in {\mathbb {R}}^K\) from a target value \(y^*\) of the chemical propery \(\pi \) such that \(\psi _{{\mathcal {N}}}(x^*)=y^*\). Based on the method due to Akutsu and Nagamochi [16], Chiewvanichakorn et al. [18] showed that this problem can be formulated as an MILP. By including a set of linear constraints such that \(x\in {\mathcal {D}}\) into their MILP, we obtain the next result.
Theorem 1
([20, 21]) Let \({{\mathcal {N}}}\) be an ANN with a piecewiselinear activation function for an input vector \(x\in {\mathbb {R}}^K,\) \(n_A\) denote the number of nodes in the architecture and \(n_B\) denote the total number of breakpoints over all activation functions. Then there is an MILP \({{\mathcal {M}}}(x,y;{\mathcal {C}}_1)\) that consists of variable vectors \(x\in {\mathcal {D}}~(\subseteq {\mathbb {R}}^K)\), \(y\in {\mathbb {R}}\), and an auxiliary variable vector \(z\in {\mathbb {R}}^p\) for some integer \(p=O(n_A+n_B)\) and a set \({\mathcal {C}}_1\) of \(O(n_A+n_B)\) constraints on these variables such that: \(\psi _{{{\mathcal {N}}}}(x^*)=y^*\) if and only if there is a vector \((x^*,y^*)\) feasible to \({{\mathcal {M}}}(x,y;{\mathcal {C}}_1)\).
See Appendix “Upper and lower bounds on descriptors” for the set of constraints to define our AD \({\mathcal {D}}\) in the MILP \({{\mathcal {M}}}(x,y;{\mathcal {C}}_1)\) in Theorem 1.
A vector \(x\in {\mathbb {R}}^K\) is called admissible if there is a chemical graph \(G\in {\mathcal {G}}\) such that \(f(G)=x\) [17]. Let \({\mathcal {A}}\) denote the set of admissible vectors \(x\in {\mathbb {R}}^K\). To ensure that a vector \(x^*\) inferred from a given target value \(y^*\) becomes admissible, we introduce a new vector variable \(g\in {\mathbb {R}}^{q}\) for an integer q. For the class \({\mathcal {G}}\) of chemical acyclic graphs, Azam et al. [17] introduced a set \({\mathcal {C}}_2\) of new constraints with a new vector variable \(g\in {\mathbb {R}}^{q}\) for an integer q so that

A feasible solution \((x^*,g^*)\) of a new MILP for a target value \(y^*\) delivers a vector \(x^*\) with \(\psi _{{{\mathcal {N}}}}(x^*)=y^*\), and

A vector \(g^*\) that represents a chemical acyclic graph \(G^*\in {\mathcal {G}}\).
Afterwards, for the classes of chemical graphs with cycle index 1 and 2, Ito et al. [17] and Zhu et al. [21] presented such a set \({\mathcal {C}}_2\) of constraints so that a vector \(g^*\) in a feasible solution \((x^*,g^*)\) of a new MILP can represent a chemical graph \(G^*\) in the class \({\mathcal {G}}\), respectively.
As the second phase, we solve (II) Inverse Problem for the inverse QSAR/QSPR by treating the following inference problems.
(IIa) Inference of Vectors
Input: A real \(y^*\) with \({\underline{a}}\le y^*\le {\overline{a}}\).
Output: Vectors \(x^*\in {\mathcal {A}}\cap {\mathcal {D}}\) and \(g^*\in {\mathbb {R}}^{q}\) such that \(\psi _{{\mathcal {N}}}(x^*)=y^*\) and \(g^*\) forms a chemical graph \(G^*\in {\mathcal {G}}\) with \(f(G^*)=x^*\).
(IIb) Inference of Graphs
Input: A vector \(x^*\in {\mathcal {A}}\cap {\mathcal {D}}\).
Output: All graphs \(G^*\in {\mathcal {G}}\) such that \(f(G^*)=x^*\).
The second phase consists of the next two steps.
Phase 2.
Stage 4: Formulate Problem (IIa) as the above MILP \({{\mathcal {M}}}(x,y,g;{\mathcal {C}}_1,{\mathcal {C}}_2)\) based on \({\mathcal {G}}\) and \({{\mathcal {N}}}\). Find a feasible solution \((x^*,g^*)\) of the MILP such that

\(x^*\in {\mathcal {A}}\cap {\mathcal {D}}\) and \(\psi _{{\mathcal {N}}}(x^*)=y^*\).
The second requirement may be replaced with inequalities \((1\varepsilon )y^* \le \psi _{{\mathcal {N}}}(x^*) \le (1+\varepsilon )y^*\) for a tolerance \(\varepsilon >0.\)
Stage 5: To solve Problem (IIb), enumerate all (or a specified number) of graphs \(G^*\in {\mathcal {G}}\) such that \(f(G^*)=x^*\) for the inferred vector \(x^*\). See Fig. 3d, e for an illustration of Stages 4 and 5 in Phase 2.
In practical applications, there would be many criteria that a target chemical compound needs to satisfy rather than a single chemical property \(\pi \), such as stability and synthesizability. The above five steps in the framework are rather schematic in the sense that it would be necessary to adjust several settings in each stage in order to find a collection of chemical graphs that meet many of those criteria after a repeated application of the framework. For example, we can include in an MILP formulation in Stage 4 additional conditions such as lower and upper bounds on the frequency of adjacencyconfigurations and extra requirements on substructures of a target chemical graph as long as these conditions can be expressed as linear constraints with integer/real variables. Also an efficient algorithm in Stage 5 can quickly offer a large number of isomers of the same feature vectors, to which we can apply a further screening to choose promising candidates for chemical graphs.
Our target graph class
In this paper, we choose a branchparameter \(k\ge 1\) and define a class \({\mathcal {G}}\) of chemical acyclic graphs G such that

The maximum degree in G is at most 4;

The kbranch height \(\text {bh}_k(G)\) is bounded for a specified branchparameter k; and

The size of each kfringetree in G is bounded.
The reason why we restrict ourselves to the graphs in \({\mathcal {G}}\) is that this class \({\mathcal {G}}\) covers a large part of the acyclic chemical compounds registered in the chemical database PubChem. See Appendix A for a summary of the statistical features of the chemical graphs in PubChem in terms of kbranch height and the size of 2fringetrees. According to this, over 55% (resp., 99%) of acyclic chemical compounds with up to 100 nonhydrogen atoms in PubChem have the maximum degree 3 (resp., 4); and nearly 87% (resp., 99%) of acyclic chemical compounds with up to 50 nonhydrogen atoms in PubChem have the 2branch height at most 1 (resp., 2). This implies that \(k=2\) is sufficient to cover most of chemical acyclic graphs. For \(k=2\), over 92% of 2fringetrees of chemical compounds with up to 100 nonhydrogen atoms in PubChem obey the following size constraint:
We formulate an MILP in Stage 4 that, given a target value \(y^*\), infers a vector \(x^*\in {\mathbb {Z}}_+^K\) with \(\psi _{{\mathcal {N}}}(x^*)=y^*\) and a chemical acyclic graph \(G^*=(H,\alpha ,\beta )\in {\mathcal {G}}\) with \(f(G^*)=x^*\). We here specify some of the features of a graph \(G^*\in {\mathcal {G}}\) such as the number of nonhydrogen atoms in order to control the graph structure of target graphs to be inferred and to simplify MILP formulations. In this paper, we specify the following features on a graph \(G\in {\mathcal {G}}\): a set \(\Lambda \) of chemical elements, a set \(\Gamma _{<}\) of adjacencyconfigurations, the maximum degree, the number of nonhydrogen atoms, the diameter, the kbranch height and the kbranchleaf number for a branchparameter k.
More formally, given specified integers \(n^*\), \(d_\text {max}\), \(\text {dia}^*\), \(k^*\), \(\text {bh}^*\), \({\text {bl}}^*\in {\mathbb {Z}}\) other than \(\Lambda \) and \(\Gamma \), let \({\mathcal {H}}(n^*, d_\text {max}, \text {dia}^*, k^*, \text {bh}^*, {\text {bl}}^*)\) denote the set of acyclic graphs H such that

The maximum degree of a vertex in H is at most 3 when \(d_\text {max}=3\) (or equal to 4 when \(d_\text {max}=4\)),

The number n(H) of vertices in H is \(n^*\),

The diameter \(\text {dia}(H)\) of H is \(\text {dia}^*\),

The \(k^*\)branch height \(\text {bh}_{k^*}(H)\) is \(\text {bh}^*\),

The \(k^*\)branchleaf number \({\text {bl}}_{k^*}(H)\) is \({\text {bl}}^*\) and

(1) holds.
To design Stage 4 for our class \({\mathcal {G}}\), we formulate an MILP \({{\mathcal {M}}}(x,g; {\mathcal {C}}_2)\) that infers a chemical graph \(G^*=(H,\alpha ,\beta )\in {\mathcal {G}}\) with \(H\in {\mathcal {H}}(n^*, d_\text {max}, \text {dia}^*, k^*, \text {bh}^*, {\text {bl}}^*)\) for a given specification \((\Lambda ,\Gamma ,n^*, d_\text {max}, \text {dia}^*, k^*, \text {bh}^*, {\text {bl}}^*)\). The details will be given in "MILPs for chemical acyclic graphs with bounded branchheight" section and Appendix C.
Design of Stage 5, i.e., generating chemical graphs \(G^*\) that satisfy \(f(G^*)=x^*\) for a given feature vector \(x^*\in {\mathbb {Z}}_+^K\) is still challenging for a relatively large instance with size \(n(G^*)\ge 20\). There have been proposed algorithms for generating chemical graphs \(G^*\) in Stage 5 for the classes of graphs with cycle index 0 to 2 [5, 22,23,24]. All of these are designed based on the branchandbound method and can generate a target chemical graph with size \(n(G^*)\le 20\). To break this barrier, we newly employ the dynamic programming method for designing an algorithm in Stage 5 in order to generate a target chemical graph \(G^*\) with size \(n(G^*)=50\). For this, we further restrict the structure of acyclic graphs G so that the number \({\text {bl}}_2(G)\) of leaf 2branches is at most 3. Among all acyclic chemical compounds with up to 50 nonhydrogen atoms in the chemical database PubChem, the ratio of the number of acyclic chemical compounds G with \({\text {bl}}_2(G)\le 2\) (resp., \({\text {bl}}_2(G)\le 3\)) is 78% (resp., 95%). See "A new graph search algorithm" section and Appendix D for the details on the new algorithm in Stage 5.
To conclude the description of the target graph class to be inferred by the inverse QSAR/QSPR framework developed in this paper, we summarize the global parameters in Table 3.
MILPs for chemical acyclic graphs with bounded branchheight
In this section, we describe an idea of formulating an MILP \({{\mathcal {M}}}(x,g;{\mathcal {C}}_2)\) to infer a chemical acyclic graph G in the class \({\mathcal {G}}\) for a given specification \((\Lambda ,\Gamma ,n^*, d_\text {max}, \text {dia}^*,\) \( k^*, \text {bh}^*, {\text {bl}}^*)\) defined in the previous section. Please refer to Table 3 for a summary of the parameters that we assume to be fixed for a target graph.
Scheme graphs
Our new idea of constructing an acyclic graph H is as follows. See a rooted tree \(T_B=T(d_\text {max},d_\text {max}1,\text {bh}^*)\) in Fig. 4a.

From the tree \(T_B\), we first choose a subtree T including the root \(u_1\). We use T as the \(k^*\)branchtree of H.

Next, we choose some edges in the tree T and replace each of the edges \(e=u_i u_j\) with a path \(P_e\) between vertices \(u_i\) and \(u_j\). Let \(T^*\) denote the resulting tree. We use \(T^*\) as the \(k^*\)branchsubtree of H.

Finally, we append to the tree \(T^*\) rooted trees with height at most k as the \(k^*\)fringetrees of H. The resulting tree is a required rooted tree H.
In our MILP, we prepare a binary variable for each of the vertices and edges in \(T_B\) so that a subtree T of \(T_B\) can be selected as one of the combinations of these binary values.
To represent a replacement of an edge e with a path \(P_e\) in our MILP, we introduce a path \(P_{t^*}=(v_{1,1},v_{2,1},\ldots ,v_{t^*,1})\) of a sufficiently large length \(t^*1\), and a set F of directed edges between the vertices in \(T_B\) and \(P_{t^*}\) as shown in Fig. 4a. We also introduce a binary variable for each of the vertices and edges in \(P_{t^*}\) and F in our MILP. When an edge \(e=u_i u_j\) is replaced with a path \(P_e\), we select an edge from \(u_i\) to a vertex \(v_{h,1}\) in \(P_{t^*}\) and an edge from a vertex \(v_{h+p,1}\) so that the edges \((u_i,v_{h,1})\) and \((v_{h+p,1},u_j)\) and the subpath \((v_{h,1},v_{h+1,1},\ldots , v_{h+p,1})\) of \(P_{t^*}\) form a path \(P_e\). Such a path \(P_e\) can be selected as one of the combinations of these binary values. To append rooted trees to tree \(T^*\), we prepare a rooted tree with a sufficiently large size at each vertex in \(T_B\) and \(P_{t^*}\) and introduce a binary variable for each of the vertices and edges in these rooted trees in our MILP. A rooted subtree from each of such rooted trees as a \(k^*\)fringetree can be selected as one of the combinations of these binary values.
We call the graph that consists of all the above graphs \(T_B\), \(P_{t^*}\) and the edge set F and the set of rooted trees at the vertices in \(T_B\) and \(P_{t^*}\) a scheme graph \(\text {SG}( d_\text {max}, k^*, \text {bh}^*, t^*)\).
Figure 5a illustrates an acyclic graph H with \(n(H)=37\), \(\text {dia}(H)=17\), \(\text {bh}_2(H)=2\) and \({\text {bl}}_2(H)=3\), where the maximum degree of a vertex is 3. Figure 5b illustrates the 2branchtree of the acyclic graph H in Fig. 5a. Figure 5c illustrates a subgraph \(H'\) of the scheme graph \(\text {SG}( d_\text {max}, k^*, \text {bh}^*, t^*=n^*{\text {bl}}^*1)\) such that \(H'\) is isomorphic to the acyclic graph H in Fig. 5a.
In this paper, we obtain the following result.
Theorem 2
Let \(\Lambda \) be a set of chemical elements, \(\Gamma \) be a set of adjacencyconfigurations, where \(\Lambda \le \Gamma \), and \(K = 2\Lambda  + 2\Gamma  + 50\). Given nonnegative integers \(n^*\ge 3\), \(d_\text {max}\in \{3,4\}\), \(\text {dia}^*\ge 3\), \(k^*\ge 1\), \(\text {bh}^*\ge 1\) and \({\text {bl}}^*\ge 2\), there is an MILP \({{\mathcal {M}}}(x,g;{\mathcal {C}}_2)\) that consists of variable vectors \(x\in {\mathbb {R}}^K\) and \(g\in {\mathbb {R}}^q\) for an integer \(q=O( \Gamma \cdot [ (d_\text {max}1)^{\text {bh}^*+k^*} +n^*\cdot (d_\text {max}1)^{\max \{\text {bh}^*,k^*\}})])\) and a set \({\mathcal {C}}_2\) of constraints on x and g with size \(O(\Gamma  + (d_\text {max}1)^{\text {bh}^*+k^*} +n^*\cdot (d_\text {max}1)^{\max \{\text {bh}^*, k^*\}}) )\) such that: \((x^*, g^*)\) is feasible to \({{\mathcal {M}}}(x,g; {\mathcal {C}}_2)\) if and only if \(g^*\) forms a chemical acyclic graph \(G=(H,\alpha ,\beta )\) such that \(H\in {\mathcal {H}}(n^*, d_\text {max}, \text {dia}^*, k^*, \text {bh}^*,{\text {bl}}^*)\) and \(f(G)=x^*\).
Note that our MILP requires only \(O(n^*)\) variables and constraints when the branchparameter \(k^*\), the \(k^*\)branch height and \(\Gamma \) are constant.
See Appendices B and C for the details of the MILP formulation and the set of all variables and constraints in the MILP formulation, respectively.
A new graph search algorithm
Previous methods of inferring chemical graphs [17,18,19] use a graph search algorithm based on the branchandbound algorithm proposed by Fujiwara et al. [5], where an enormous number of chemical graphs are constructed by repeatedly appending and removing a vertex one by one until a target chemical graph is constructed. Their algorithm cannot generate even one acyclic chemical graph when n(G) is larger than around 20.
This section introduces a new dynamic programming method for designing an algorithm in Stage 5. We consider the following aspects:

(a)
Treat acyclic graphs with a certain limited structure that frequently appears among chemical compounds registered in the chemical database; and

(b)
Instead of manipulating acyclic graphs directly, first compute the frequency vectors \(\pmb {f}(G')\) (subvectors of the feature vectors \(f(G')\), see Appendix D) of subtrees \(G'\) of all target acyclic graphs and then construct a limited number of target graphs G from the process of computing the vectors.
In (a), we choose a branchparameter \(k^*=2\) and treat acyclic graphs G that have a small 2branch number such as \({\text {bl}}_2(G)\in [2,3]\) and satisfy the size constraint (1) on 2fringetrees. Figure 6a, b illustrate chemical acyclic graphs G with \({\text {bl}}_2(G)=2\) and \({\text {bl}}_2(G)= 3\), respectively.
We design a method in (b) based on the mechanism of dynamic programming in the following way. Define a frequency vector \(\pmb {f}(T)\) of each chemical rooted tree T to be a vector that consists of the frequency of each chemical element \({\mathtt{a}}\in \Lambda \), each adjacencyconfiguration \({\mathtt{a}}\in \Lambda \), each bondconfiguration \(\mu \in \text {Bc}\), and each degree \(\text {dg}i\in \text {Dg}\) in T. We are given a vector \(\pmb {x}^*\) that is the frequency vector \(\pmb {f}(G)\) of a chemical acyclic graph G to be inferred.
We first construct a set \(\text {FT}\) of chemical rooted trees with height at most \(k^*=2\) and compute the frequency vector \(\pmb {f}(T)\) of each chemical rooted tree \(T\in \text {FT}\) to obtain the set \(\text {W}(\text {FT})\) of frequency vectors \(\pmb {f}(T), T\in \text {FT}\). Note that a large number of chemical rooted trees \(T\in \text {FT}\) maps to the same frequency vector \(\pmb {w}\) and the size \(\text {W}(\text {FT})\) is considerably smaller than the size \(\text {FT}\).
We next combine two chemical rooted trees \(T_a,T_b\in \text {FT}\) to construct a chemical tree \(T_{a,b}\) by joining their roots \(r_a\) and \(r_b\) with an edge \(e=r_a r_b\) of a bondmultiplicity m, as illustrated in Fig. 6a. In fact, we compute only the feature vector \(\pmb {f}(T_{a,b})\) of such a tree \(T_{a,b}\) without directly treating the graph structures of \(T_a\), \(T_b\) and \(T_{a,b}\). For this, we add two frequency vectors \(\pmb {w}_a,\pmb {w}_b\in \text {W}(\text {FT})\) together with an additional term from the bondmultiplicity m to obtain the frequency vector \(\pmb {w}_{a,b}~(=\pmb {f}(T_{a,b}))\) of such a tree \(T_{a,b}\). Given such a vector \(\pmb {w}_{a,b}\), we can actually construct a chemical tree \(T_{a,b}\) with \(\pmb {f}(T_{a,b})=\pmb {w}_{a,b}\) by choosing trees \(T_a,T_b\in \text {FT}\) and combining them with an edge of bondmultiplicity m.
Our algorithm for generating a chemical acyclic graph G with \({\text {bl}}_2(G)=2\) continues to compute a set \(\text {W}^{(p)}\) of frequency vectors of chemical trees that can be obtained by combining p trees in \(\text {FT}\) for each \(p=2,3,\ldots , \lceil (\text {dia}^*5)/2\rceil \). Finally, we find a vector pair \((\pmb {w}^1,\pmb {w}^2)\) with \(\pmb {w}^1\in \text {W}^{(\lfloor (\text {dia}^*5)/2\rfloor )}\) and \(\pmb {w}^2\in \text {W}^{(\lceil (\text {dia}^*5)/2\rceil )}\) such that a vector with \(\pmb {w}^1\), \(\pmb {w}^2\) and a bondmultiplicity m is equal to the given vector \(\pmb {x}^*\); i.e., a chemical acyclic graph G with \(\pmb {f}(G)=\pmb {x}^*\) is obtained by joining chemical trees \(T^1\) and \(T^2\) with \(\pmb {w}^i=\pmb {f}(T_i), i=1,2\) with an edge of bondmultiplicity m.
With a slight modification, the algorithm can generate a chemical acyclic graph G with \({\text {bl}}_2(G)=3\).
Appendix D presents the details of our new algorithms for generating acyclic graphs G with \({\text {bl}}_2(G)\in [2,3]\).
Experimental results
We implemented our method of Stages 1 to 5 for inferring chemical acyclic graphs and conducted experiments to evaluate the computational efficiency for three chemical properties \(\pi \): octanol/water partition coefficient (Kow), boiling point (Bp) and heat of combustion (Hc). We executed the experiments on a PC with Two Intel Xeon CPUs E51660 v3 @3.00GHz, 32 GB of RAM running under OS: Ubuntu 14.04.6 LTS. We show 2D drawings of some of the inferred chemical graphs, where ChemDoodle version 10.2.0 was used for constructing the drawings.
Results on Phase 1. We implemented Stages 1, 2, and 3, in Phase 1 as follows.
Stage 1. We set a graph class \( {\mathcal {G}}\) to be the set of all chemical acyclic graphs, and set a branchparameter \(k^*\) to be 2. For each property \(\pi \in \{\) Kow, Bp, Hc\(\}\), we first select a set \(\Lambda \) of chemical elements and then collected a data set \(D_{\pi }\) on chemical acyclic graphs over the set \(\Lambda \) of chemical elements provided by the Hazardous Substances Data Bank (HSDB) of PubChem. To construct the data set, we eliminated chemical compounds that have at most three carbon atoms or contain a charged element such as \(\mathtt{N}^+\) or an element \({\mathtt{a}}\in \Lambda \) whose valence is different from our setting of valence function \({\text {val}}\).
Table 4 shows the size and range of data sets that we prepared for each chemical property in Stage 1, where we denote the following:

\(\pi \): one of the chemical properties Kow, Bp and Hc;

\(\Lambda \): the set of selected chemical elements (hydrogen atoms are added at the final stage);

\(D_{\pi }\): the size of data set \(D_{\pi }\) over \(\Lambda \) for property \(\pi \);

\(\Gamma \): the number of different adjacencyconfigurations over the compounds in \(D_{\pi }\);

\([{\underline{n}},{\overline{n}}]\): the minimum and maximum number n(G) of nonhydrogen atoms over the compounds G in \(D_{\pi }\);

\([{\underline{{\text {bl}}}},\overline{{\text {bl}}}]\): the minimum and maximum numbers \({\text {bl}}_2(G)\) of leaf 2branches over the compounds G in \(D_{\pi }\);

\([{\underline{\text {bh}}},\overline{\text {bh}}]\): the minimum and maximum values of the 2branch height \(\text {bh}_2(G)\) over the compounds G in \(D_{\pi }\); and

\([{\underline{a}},{\overline{a}}]\): the minimum and maximum values of a(G) for \(\pi \) over compounds G in \(D_{\pi }\).
Stage 2. We used a feature function f that consists of the descriptors defined in “Descriptors” section.
Stage 3. We used scikitlearn version 0.21.6 with Python 3.7.4 to construct ANNs \({{\mathcal {N}}}\) where the tool and activation function are set to be MLPRegressor and ReLU, respectively. We tested several different architectures of ANNs for each chemical property. To evaluate the performance of the resulting prediction function \(\psi _{{\mathcal {N}}}\) with crossvalidation, we partition a given data set \(D_{\pi }\) into five subsets \(D_{\pi }^{(i)}\), \(i\in [1,5]\) randomly, where \(D_{\pi }\setminus D_{\pi }^{(i)}\) is used for a training set and \(D_{\pi }^{(i)}\) is used for a test set in five trials \(i\in [1,5]\). For a set \(\{y_1,y_2,\ldots ,y_N\}\) of observed values and a set \(\{\psi _1,\psi _2,\ldots ,\psi _N\}\) of predicted values, we define the coefficient of determination to be \(\text {R}^2\triangleq 1 \frac{\sum _{j\in [1,N]}(y_j\psi _j)^2}{\sum _{j\in [1,N]}(y_j{\overline{y}})^2}\), where \({\overline{y}}= \frac{1}{N}\sum _{j\in [1,N]}y_j\). Table 5 shows the results on Stages 2 and 3, where

K: the number of descriptors for the chemical compounds in data set \(D_{\pi }\) for property \(\pi \);

Activation: the choice of activation function;

Architecture: (a, b, 1) consists of an input layer with a nodes, a hidden layer with b nodes and an output layer with a single node, where a is equal to the number K of descriptors;

Ltime: the average time (in seconds) to construct ANNs for each trial;

test \(\text {R}^2\) (ave.): the average of coefficient of determination over the five tests; and

test \(\text {R}^2\) (best): the largest value of coefficient of determination over the five test sets.
From Table 5, we see that the execution of Stage 3 was successful, where the average of test \(\text {R}^2\) is over 0.9 for all three chemical properties.
For each chemical property \(\pi \), we selected the ANN \({{\mathcal {N}}}\) that attained the best test \(\text {R}^2\) score among the five ANNs to formulate an MILP \({{\mathcal {M}}}(x,y,z;{\mathcal {C}}_1)\) which will be used in Phase 2.
Results on Phase 2. We implemented Stages 4 and 5 in Phase 2 as follows.
Stage 4. In this step, we solve the MILP \({{\mathcal {M}}}(x,y,g;{\mathcal {C}}_1,{\mathcal {C}}_2)\) formulated based on the ANN \({{\mathcal {N}}}\) obtained in Phase 1. To solve an MILP in Stage 4, we use CPLEX version 12.10. In our experiment, we choose a target value \(y^* \in [{\underline{a}}, {\overline{a}}]\) and fix or bound some descriptors in our feature vector as follows:

Set the 2leafbranch number \({\text {bl}}^*\) to be each of 2 and 3;

Fix the instance size \(n^*=n(G)\) to be each integer in \(\{26,32,38,44,50\}\);

Set the diameter \(\text {dia}^*=\text {dia}(G)\) be one of the integers in \(\{ \lceil (2/5)n^*\rceil , \lceil (3/5)n^*\rceil \}\).

Set the maximum degree \(d_\text {max}:=3\) for \(\text {dia}^*=\lceil (2/5)n^*\rceil \) and \(d_\text {max}:=4\) for \(\text {dia}^*= \lceil (3/5)n^*\rceil \);

For each instance size \(n^*\), test a target value \(y^*_{\pi }\) for each chemical property \(\pi \in \{\) Kow, Bp, Hc\(\}\).
Based on the above setting, we generated six instances for each instance size \(n^*\). We set \(\varepsilon =0.02\) in Stage 4.
Tables 6, 7 (resp., Tables 8, 9) show the results on Stage 4 for \({\text {bl}}^*=2\) (resp., \({\text {bl}}^*=3\)), where we denote the following:

\(y^*_{\pi }\): a target value in \([{\underline{a}},{\overline{a}}]\) for a property \(\pi \);

\(n^*\): a specified number of vertices in \([{\underline{n}},{\overline{n}}]\);

\(\text {dia}^*\): a specified diameter in \(\{ \lceil (2/5)n^*\rceil , \lceil (3/5)n^*\rceil \}\);

IPtime: the time (sec.) to an MILP instance to find vectors \(x^*\) and \(g^*\).
We observe that most of the MILP instances with \({\text {bl}}^*=2\), \(n^*\le 50\) and \(\text {dia}^*\le 30\) (resp., \({\text {bl}}^*=3\), \(n^*\le 50\) and \(\text {dia}^*\le 30\)) are solved within one minute (resp., in a few minutes). The previously most efficient MILP formulation for inferring chemical acyclic graphs due to Zhang et al. [19] could solve instances with a relatively small diameter of \(\text {dia}^*=9\) for the case of \(d_\text {max}=4\) and \(n^*=20\) and \(\text {dia}^*=8\) for the case of \(d_\text {max}=3\) and \(n^*=50\). Our new MILP formulation on chemical acyclic graphs with bounded 2branch height considerably improved the tractable size of chemical acyclic graphs in Stage 4 for the inference problem (IIa).
Figure 7a–c illustrate some chemical acyclic graphs G with \({\text {bl}}_2(G)=2\) obtained in Stage 4 by solving an MILP. Remember that these chemical graphs obey the AD \({\mathcal {D}}\) defined in Appendix A.
Figure 8a–c illustrate some chemical acyclic graphs G with \({\text {bl}}_2(G)=3\) obtained in Stage 4 by solving an MILP.
Stage 5. In this stage, we execute our new graph search algorithms for generating target graphs \(G\in {\mathcal {G}}(\pmb {x}^*)\) with \({\text {bl}}_2(G)\in \{2,3\}\) for a given feature vector \(\pmb {x}^*\) obtained in Stage 4.
We introduce a time limit of 10 minutes for each iteration h in Step 2 and an execution of Steps 1 and 3 for \({\text {bl}}^*=2\) (resp., each iteration h in Steps 2 and 3 and \(\delta _1\) in Step 4 and an execution of Steps 1 and 5 for \({\text {bl}}^*=3\)). In the last step, we choose at most 100 feasible vector pairs and generate a target graph from each of these feasible vector pairs. We also impose an upper bound \(\text {UB}\) on the size \(\text {W}\) of a vector set \(\text {W}\) that we maintain during an execution of the algorithm. We executed the algorithm for each of the three bounds \(\text {UB}=10^6, 10^7, 10^8\) until a feasible vector pair is found or the running time exceeds a global time limitation of two hours.
When no feasible vector pair is found by the graph search algorithms, we output the target graph \(G^*\) constructed from the vector \(g^*\) in Stage 4.
Tables 6, 7 (resp., Tables 8, 9) show the results of Stage 5 for \({\text {bl}}^*=2\) (resp., \({\text {bl}}^*=3\)), where we denote the following:

\(\#\)FP: the number of feasible vector pairs obtained by an execution of the graph search algorithm for a given feature vector \(\pmb {x}^*\);

GLB: a lower bound on the number of all target graphs \(G\in {\mathcal {G}}(\pmb {x}^*)\) for a given feature vector \(\pmb {x}^*\);

\(\#\)G: the number of all (or up to 100) chemical acyclic graphs G such that \(f(G)=x^*\) (where at least one such graph G has been found from the vector \(g^*\) in Stage 4);

Gtime: the running time (sec.) to execute Stage 5 for a given feature vector \(\pmb {x}^*\), where “> 2 hours” means that the running time exceeds two hours.
Previously, an instance of chemical acyclic graphs with size \(n^*\) up to 16 was solved in Stage 5 by Azam et al. [17]. For the classes of chemical graphs with cycle index 1 and 2, the maximum size of instances solved in Stage 5 by Ito et al. [17] and Zhu et al. [21] was around 18 and 15, respectively. Our new algorithm based on dynamic programming solves instances with \(n^*=50\). In our experiments, we also computed a lower bound GLB on the number of target graphs. We observe that there are over \(10^{10}\) or \(10^{14}\) target graphs in some cases. Remember that these lower bounds are computed without actually generating each target graph one by one. So when a lower bound is enormously large, this would suggest that we may need to impose some more constraints on the structure of graphs or the range of descriptors to narrow a family of target graphs to be inferred.
An additional experiment We also conducted some additional experiment to demonstrate that our MILPbased method is flexible to control conditions on inference of chemical graphs. In Stage 3, we constructed an ANN \({{\mathcal {N}}}_{\pi }\) for each of the three chemical properties \(\pi \in \{\) Kow, Bp, Hc\(\}\), and formulated the inverse problem of each ANN \({{\mathcal {N}}}_{\pi }\) as an MILP \({{\mathcal {M}}}_{\pi }\). Since the set of descriptors is common to all three properties Kow, Bp and Hc, it is possible to infer a chemical acyclic graph G that satisfies a target value \(y^*_{\pi }\) for each of the three properties at the same time (if one exists). We specify the size of graph so that \(n^* =50\), \({\text {bl}}^* =2\), \(\text {dia}^* = 25\) and \(d_\text {max}=4\), and set target values with \(y^*_{\text {Kow}} =4.0\), \(y^*_{\text {Bp}} =400.0\) and \(y^*_{\text {Hc}} =13000.0\) in an MILP that consists of the three MILP \({{\mathcal {M}}}_{\text {Kow}}\), \({{\mathcal {M}}}_{\text {Hc}}\) and \({{\mathcal {M}}}_{\text {Bp}}\). The MILP was solved in 18930 seconds and we obtained a chemical acyclic graph G illustrated in Fig. 9. We continued to execute Stage 5 for this instance to generate more target graphs \(G^*\). Table 10 shows that 100 target graphs are generated by our new dynamic programming algorithm.
Concluding remarks
In this paper, we introduced a new measure, branchheight of a tree, and showed that many chemical compounds in the chemical database have a simple structure where the number of 2branches is small. Based on this, we proposed a new method of applying the framework for inverse QSAR/QSPR [17,18,19] to the case of acyclic chemical graphs where Azam et al. [17] inferred chemical graphs with around 20 nonhydrogen atoms and Zhang et al. [19] solved an MILP of inferring a feature vector for an instance with diameter 9. In our method, we formulated a new MILP in Stage 4 specialized for acyclic chemical graphs with a small branch number and designed a new graph search algorithm in Stage 5 that computes frequency vectors of graphs in a dynamic programming scheme.
We implemented our new method and conducted some experiments on chemical properties such as octanol/water partition coefficient, boiling point and heat of combustion.
The resulting method improved the performance so that chemical graphs with around 50 nonhydrogen atoms and around diameter 30 can be inferred. Since there are many acyclic chemical compounds having large diameters, this is a significant improvement.
It is left as a future work to design MILPs and graph search algorithms based on the new idea of the paper for classes of graphs with a higher rank. Recently, a method for inferring a chemical cyclic graph with any rank has been designed by Akutsu and Nagamochi [27] based on the ideas in this paper. The method is also designed so that a target chemical graph to be inferred can be specified in a more flexible way, where we can include a prescribed substructure of graphs such as a benzene ring into a target chemical graph while imposing constraints on a global topological structure of a target graph at the same time.
Availablity of data and materials
Source code of the implementation of our algorithm is freely available from https://github.com/kudml/molinfer.
Abbreviations
 ANN:

Artificial neural network
 MILP:

Mixed integer linear programming
References
 1.
Miyao T, Kaneko H, Funatsu K. Inverse QSPR/QSAR analysis for chemical structure generation (from y to x). J Chem Inf Model. 2016;56(2):286–99.
 2.
Skvortsova MI, Baskin II, Slovokhotova OL, Palyulin VA, Zefirov NS. Inverse problem in QSAR/QSPR studies for the case of topological indices characterizing molecular shape (Kier indices). J Chem Inf Comput Sci. 1993;33(4):630–4.
 3.
Ikebata H, Hongo K, Isomura T, Maezono R, Yoshida R. Bayesian molecular design with a chemical language model. J Comput Aided Mol Design. 2017;31(4):379–91.
 4.
Rupakheti C, Virshup A, Yang W, Beratan DN. Strategy to discover diverse optimal molecules in the small molecule universe. J Chem Inf Model. 2015;55(3):529–37.
 5.
Fujiwara H, Wang J, Zhao L, Nagamochi H, Akutsu T. Enumerating treelike chemical graphs with given path frequency. J Chem Inf Model. 2008;48(7):1345–57.
 6.
Kerber A, Laue R, Grüner T, Meringer M. MOLGEN 4.0. Match Commun Math Comput Chem. 1998;37:205–8.
 7.
Li J, Nagamochi H, Akutsu T. Enumerating substituted benzene isomers of treelike chemical graphs. IEEE/ACM Trans Comput Biol Bioinf. 2016;15(2):633–46.
 8.
Reymond JL. The chemical space project. Accounts Chem Res. 2015;48(3):722–30.
 9.
Akutsu T, Fukagawa D, Jansson J, Sadakane K. Inferring a graph from path frequency. Discrete Appl Math. 2012;160(10–11):1416–28.
 10.
Nagamochi H. A detachment algorithm for inferring a graph from path frequency. Algorithmica. 2009;53(2):207–24.
 11.
Bohacek RS, McMartin C, Guida WC. The art and practice of structurebased drug design: a molecular modeling perspective. Med Res Rev. 1996;16(1):3–50.
 12.
GómezBombarelli R, Wei JN, Duvenaud D, HernándezLobato JM, SánchezLengeling B, Sheberla D, AguileraIparraguirre J, Hirzel TD, Adams RP, AspuruGuzik A. Automatic chemical design using a datadriven continuous representation of molecules. ACS Central Sci. 2018;4(2):268–76.
 13.
Segler MHS, Kogej T, Tyrchan C, Waller MP. Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Central Sci. 2017;4(1):120–31.
 14.
Yang X, Zhang J, Yoshizoe K, Terayama K, Tsuda K. ChemTS: an efficient python library for de novo molecular generation. Sci Technol Adv Mater. 2017;18(1):972–6.
 15.
Kusner MJ, Paige B, HernándezLobato JM. Grammar variational autoencoder. In: Proceedings of the 34th International Conference on Machine Learning, vol 70; 2017. p. 1945–54
 16.
Akutsu T, Nagamochi H. A mixed integer linear programming formulation to artificial neural networks. In: Proceedings of the 2nd international conference on information science and systems, Tokyo, Japan, ACM; 2019. p. 215–20.
 17.
Azam NA, Chiewvanichakorn R, Zhang F, Shurbevski A, Nagamochi H, Akutsu T. A method for the inverse QSAR/QSPR based on artificial neural networks and mixed integer linear programming with guaranteed admissibility. In: Proceedings of the 13th international joint conference on biomedical engineering systems and technologies, vol 3: BIOINFORMATICS, Valetta, Malta; 2020. p. 101–108
 18.
Chiewvanichakorn R, Wang C, Zhang Z, Shurbevski A, Nagamochi H, Akutsu T. A method for the inverse QSAR/QSPR based on artificial neural networks and mixed integer linear programming. In: Proceedings of the 2020 10th international conference on bioscience, biochemistry and bioinformatics, Kyoto, Japan; 2020. p. 40–46. https://doi.org/10.1145/3386052.3386054
 19.
Zhang F, Zhu J, Chiewvanichakorn R, Shurbevski A, Nagamochi H, Akutsu T. A new integer linear programming formulation to the inverse QSAR/QSPR for acyclic chemical compounds using skeleton trees. In: Proceedings of the 33rd international conference on industrial, engineering and other applications of applied intelligent systems, Kitakyushu, Japan; 2020. p. 433–444. https://doi.org/10.1007/9783030557898_38
 20.
Ito R, Azam NA, Wang C, Shurbevski A, Nagamochi H, Akutsu T. A novel method for the inverse QSAR/QSPR to monocyclic chemical compounds based on artificial neural networks and integer programming. In: Proceedings of the 21st international conference on bioinformatics and computational biology; 2020
 21.
Zhu J, Wang C, Shurbevski A, Nagamochi H, Akutsu T. A novel method for inference of chemical compounds of cycle index two with desired properties based on artificial neural networks and integer programming. Algorithms. 13:5. doi: https://doi.org/10.3390/a13050124.124.
 22.
Suzuki M, Nagamochi H, Akutsu T. Efficient enumeration of monocyclic chemical graphs with given path frequencies. J Cheminf. 2014;6(1):31.
 23.
Tamura Y, Nishiyama Y, Wang C, Sun Y, Shurbevski A, Nagamochi H, Akutsu T. Enumerating chemical graphs with monoblock 2augmented tree structure from given upper and lower bounds on path frequencies; 2020. arXiv preprint arXiv:2004.06367
 24.
Yamashita K, Masui R, Zhou X, Wang C, Shurbevski A, Nagamochi H, Akutsu T. Enumerating chemical graphs with two disjoint cycles satisfying given path frequency specifications; 2020. arXiv preprint arXiv:2004.08381
 25.
Kim S, et al. PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Res. 2021;49(D1):D1388–95.
 26.
Netzeva TI, et al. Current status of methods for defining the applicability domain of (quantitative) structureactivity relationships: the report and recommendations of ECVAM workshop 52. Altern Lab Anim. 2005;33(2):155–73.
 27.
Nagamochi H, Akutsu T. A novel method for inference of chemical compounds with prescribed topological substructures based on integer programming; 2020. arXiv preprint arXiv:2010.09203
Acknowledgements
Not applicable.
Funding
This research was supported, in part, by Japan Society for the Promotion of Science, Japan, under Grant #18H04113.
Author information
Affiliations
Contributions
Conceptualization, HN and TA; methodology, HN; software, NAA, JZ, YS, YS, AS and L.; validation, NAA, JZ, AS and HN; formal analysis, HN; data resources, AS, LZ, HN and TA; writing—original draft preparation, HN; writing—review and editing, NAA, AS and TA; project administration, HN; funding acquisition, TA. All authors read and approved the final manuscript.
Corresponding authors
Ethics declarations
Competing interests
The authors declare that they have no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendices
Appendix A: Statistical features of molecular structures
We observe the following features of the graphtheoretical structure of chemical graphs registered in the chemical database PubChem. Let \(\text {DB}^{(\le n)}\) denote the set of chemical graphs with at most n nonhydrogen atoms that are registered in chemical database PubChem (downloaded a copy on March 21, 2019). The cycle index (or rank) of a chemical graph \(G=(H=(V,E),\alpha ,\beta )\) is defined to be \(E(V1)\) (i.e., the minimum number of edges to be removed to make the graph H acyclic). We call a chemical graph a rankr chemical graph if the rank of the graph is r. The core of a chemical cyclic graph G is defined to be the induced subgraph \(G'\) of G such that \(G'\) consists of vertices in a cycle or vertices in a path joining two cycles. A vertex in the core (not in the core) is called a core vertex (resp., a noncore vertex). The edges not in the core of a chemical cyclic graph G form a collection of trees T, which we call a noncore tree. Each noncore tree contains exactly one core vertex and is regarded as a tree rooted at the core vertex. The kbranch height of a chemical cyclic graph G is defined to be the maximum of kbranch heights over all noncore trees.
Let \(\rho _r\) (%) denote the ratio of the number of chemical graphs with rank at most \(r\in [0,4]\) to the number of all chemical graphs in PubChem. See Table 11.
Let \(\rho _0^{(d)}\) (%) denote the ratio of the number of chemical graphs in \(\text {DB}^{(\le 100)}\) such that the maximum degree is at most \(d\in [3,4]\) to the number of all chemical graphs in \(\text {DB}^{(\le 100)}\). Let \(\rho _r^{(d)}\) (%), \(r\in [1,4]\) denote the ratio of the number of rankr chemical graphs in \(\text {DB}^{(\le 100)}\) such that the maximum degree of a noncore vertex is at most \(d\in [3,4]\) to the number of all rankr chemical graphs in \(\text {DB}^{(\le 100)}\). See Table 12.
Let \(\rho _r(k,h)\) (%), \(r\in [0,4]\), \(k=2\), \(h\in [1,2]\) denote the ratio of the number of rankr chemical graphs in \(\text {DB}^{(\le 50)}\) such that the kbranch height is at most h to the number of all rankr chemical graphs in \(\text {DB}^{(\le 50)}\). See Table 13. We see that most chemical graphs G with at most 50 nonhydrogen atoms satisfy \(\text {bh}_2(G)\le 2\).
We show the distribution of 2branch height over alkans C\(_n\)H\(_{2n+2}\). Let \(\text {Aln}(n)\) denote the set of all alkans with n carbon atoms, where \(\text {Aln}(25)=36,797,588\). Let \(\rho _{\text {Aln}}(2,h)\) (%), \(h\in [1,4]\) denote the ratio of the number of alkans in \(\text {Aln}(25)\) such that the 2branch height is at most h to the number of alkans in \(\text {Aln}(25)\). See Table 14.
Let \(\rho _{\text {2bt}}(\delta )\) denote the ratio of the number of acyclic chemical graphs in \(\text {DB}^{(\le 50)}\) such that the degree of the root of the 2branchtree is \(\delta \in [1,4]\) to the number of all acyclic chemical graphs in \(\text {DB}^{(\le 50)}\). See Table 15.
Among the 2fringetrees T of all acyclic chemical graphs in \(\text {DB}^{(\le 100)}\), over \(90\%\) of them satisfy \(n\le 2d+2\) for the number \(n=V(T)\) of nonhydrogen atoms in a 2fringetree T and the number d of nonhydrogen atoms adjacent to the root in T.
Let \({\mathcal {FT}}_{0,2}\) denote the set of all 2fringetrees that appear in an acyclic chemical graph in \(\text {DB}^{(\le 100)}\), and \({\mathcal {FT}}_{0,2}^{(\delta )}\), \(\delta \in [1,3]\) denote the set of all 2fringetrees \(T\in {\mathcal {FT}}_{0,2}\) that have \(\delta \) children (i.e., the degree of the root is \(\delta \)). Let \(\rho _{2\delta +2}^{(\delta )}\) (%) denote the ratio of the number of 2fringetrees in \({\mathcal {FT}}_{0,2}^{(\delta )}\) that have at most \(2\delta +2\) vertices to the number of 2fringetrees in \({\mathcal {FT}}_{0,2}^{(\delta )}\). See Table 16.
Appendix B: Formulating an MILP based on scheme graphs
This section shows how to formulate an MILP based on a scheme graph.
Scheme graphs
Let \(t^*\), \(s^*\), and \(c^*\), be integers such that

\(t^*= n^*  (\text {bh}^* 1)  (k^*+1){\text {bl}}^*\);

\(s^*=a(b^c1)/(b1)+1\) for \(a=d_\text {max}\), \(b=d_\text {max}1\) and \(c=\text {bh}^*\); and

\(c^*=s^*1\).
Let a scheme graph \(\text {SG}( d_\text {max}, k^*, \text {bh}^*, t^*)\) consist of a tree \(T_B\), a path \(P_{t^*}\), a set \(\{S_s\mid s\in [1,s^*]\}\) of trees, a set \(\{T_t\mid t\in [1,t^*]\}\) of trees, and a set of directed edges between \(T_B\) and \(P_{t^*}\) so that an acyclic graph \(H\in {\mathcal {H}}(n^*, d_\text {max}, \text {dia}^*, k^*, \text {bh}^*, {\text {bl}}^*)\) will be constructed in the following way:

(i)
The \(k^*\)branchtree of H will be chosen as a subtree of \(T_B=(V_B,E_B)\);

(ii)
Each \(k^*\)fringetree rooted at a vertex \(u_s\in V(T_B)\) of H will be chosen as a subtree of \(S_s\);

(iii)
Each \(k^*\)branchpath of H (except for its endvertices) will be chosen as a subpath of \(P_{t^*}\) or as an edge in \(T_B\);

(iv)
Each \(k^*\)fringetree rooted at a vertex \(v_t\in V(P_{t^*})\) of H will be chosen as a subtree of \(T_t\); and

(v)
An edge (u, v) directed from \(T_B\) to \(P_{t^*}\) will be selected as an initial edge of a \(k^*\)branchpath of H and an edge (v, u) directed from \(P_{t^*}\) to \(T_B\) will be selected as an ending edge of a \(k^*\)branchpath of H.
More formally, each component of a scheme graph \(\text {SG}( d_\text {max}, k^*, \text {bh}^*, t^*)\) is defined as follows.

(i)
\(T_B =(V_B=\{u_1,u_2,\ldots ,u_{s^*}\},\) \(E_B=\{a_1,a_2,\ldots ,a_{c^*}\})\), called a basetree is a tree rooted at a vertex \(u_1\) that is isomorphic to the rooted tree \(T(d_\text {max}, d_\text {max} 1, \text {bh}^*)\). Regard \(T_B\) as an ordered tree by introducing a total order for each set of siblings and call the first (resp., last) child in a set of siblings the leftmost (resp. rightmost) child, which defines the leftmost (rightmost) path from the root \(u_1\) to a leaf in \(T_B\), as illustrated in Fig. 4a.
For each vertex \(u_s\in V_B\), let \(E_B(s)\) denote the set of indices i of edges \(a(i)\in E_B\) incident to \(u_s\) and \(\text {Cld}_B(s)\) denote the set of indices i of children \(u_i\in V_B\) of \(u_s\) in the tree \(T_B\).
For each integer \(d\in [0,k^*]\), let \(V_B(d)\) denote the set of indices s of vertices \(u_s\in V_B\) whose depth is d in the tree \(T_B\), where \(V_B(\text {bh}^*)\) is the set of indices s of leaves \(u_s\) of \(T_B\).
Regard each edge \(a_i\in E_B\) as a directed edge \((u_s,u_{s'})\) from one endvertex \(u_s\) of \(a_i\) to the other endvertex \(u_{s'}\) of \(a_i\) such that \(s=\text {prt}(s')\) (i.e., \(u_s\) is the parent of \(u_{s'}\)), where \(\text {head}(i)\) and \(\text {tail}(i)\) denote the head \(u_{s'}\) and tail \(u_s\) of edge \(a_i\in E_B\), respectively.
For each index \(s\in [1,s^*]\), let \(E_B^+(s)\) (resp., \(E_B^(s)\)) denote the set of indices i of edges \(a_i\in E_B\) such that the tail (resp., head) of \(a_i\) is vertex \(u_{s}\).
Let \(L_B\) denote the set of indices of leaves of \(T_B\), and \(s^\text {left}\) (resp., \(s^\text {right}\)) denote the index \(s\in L_B\) of the leaf \(u_s\) at which the leftmost (resp., rightmost) path from the root ends.
For each leaf \(u_s\), \(s\in L_B\), let \(V_{B,s}\) (resp., \(E_{B,s}\)) denote the set of indices s of nonroot vertices \(u_s\) (resp., indices i of edges \(a(i)\in E_B\)) along the path from the root to the leaf \(u_s\) in the tree \(T_B\).
For the example of a basetree \(T_B\) with \(\text {bh}^*=2\) in Fig. 4, it holds that \(L_B=\{5,6,7,8,9,10\}\), \(s^\text {left}=5\), \(s^\text {right}=10\), \(E_{B,s^\text {left}}=\{1,4\}\) and \(V_{B,s^\text {left}}=\{2,5\}\).

(ii)
\(S_s\), \(s\in [1,s^*]\) is a tree rooted at vertex \(u_s\in V_B\) in \(T_B\) that is isomorphic to the rooted tree \(T(d_\text {max}1, d_\text {max}1, k^*)\), as illustrated in Fig. 4b. Let \(u_{s,i}\) and \(e'_{s,i}\) denote the vertex and edge in \(S_s\) that correspond to the ith vertex and the ith edge in \(T(d_\text {max}1, d_\text {max}1, k^*)\), respectively. Regard each edge \(e'_{s,i}\) as a directed edge \((u_{s,\text {prt}(i)},u_{s,i})\). For this, each vertex \(u_s\in V_B\) is also denoted by \(u_{s,1}\).

(iii)
\(P_{t^*}=(V_P=\{v_1\), \(v_2\), \(\ldots \), \(v_{t^*}\}\), \(E_P=\{e_2\), \(e_3\), \(\ldots \), \(e_{t^*}\})\), called a linkpath with size \(t^*\) is a directed path from vertex \(v_1\) to vertex \(v_{t^*}\), as illustrated in Fig. 4a. Each edge \(e_t\in E_P\) is directed from vertex \(v_{t1}\) to vertex \(v_t\).

(iv)
\(T_t\), \(t\in [1,t^*]\) is a tree rooted at vertex \(v_t\) in \(P_{t^*}\) that is isomorphic to the rooted tree \(T(d_\text {max}2, d_\text {max}1, k^*)\), as illustrated in Fig. 4c. Let \(v_{t,i}\) and \(e_{t,i}\) denote the vertex and edge in \(T_t\) that correspond to the ith vertex and the ith edge in \(T(d_\text {max}2, d_\text {max}1, k^*)\), respectively. Regard each edge \(e_{t,i}\) as a directed edge \((v_{t,\text {prt}(i)},u_{t,i})\). For this, each vertex \(v_t\in V_P\) is also denoted by \(v_{t,1}\).

(v)
For every pair (s, t) with \(s\in [1,s^*]\) and \(t\in [1,t^*]\), join vertices \(u_{s}\) and \(v_{t}\) with directed edges \((u_{s},v_{t})\) and \((v_{t},u_{s})\), as illustrated in Fig. 4a.
We explain the basic idea of an MILP in Theorem 2. The MILP mainly consists of the following three types of constraints.

C1.
Constraints for selecting an acyclic graph H as a subgraph of the scheme graph \(\text {SG}( d_\text {max}, k^*, \text {bh}^*, t^*);\)

C2.
Constraints for assigning chemical elements to vertices and multiplicity to edges to determine a chemical graph \(G=(H,\alpha ,\beta )\); and

C3.
Constraints for computing descriptors from the selected acyclic chemical graph G.
In the constraints of C1, more formally we prepare the following.

(i)
In the scheme graph \(\text {SG}( d_\text {max}, k^*, \text {bh}^*, t^*)\), we prepare a binary variable u(s, 1) for each vertex \(u_s=u_{s,1}\in V_B\), \(s\in [1,s^*]\) so that vertex \(u_s=u_{s,1}\) becomes a \(k^*\)branch of a selected graph H if and only if \(u(s,1)=1\). The subgraph of the basetree \(T_B\) that consists of vertices \(u_s=u_{s,1}\) with \(u(s,1)=1\) will be the \(k^*\)branchtree of the graph H. We also prepare a binary variable a(i), \(i\in [1,c^*]\) for each edge \(a_i\in E_B\), where \(c^*=s^*1\). For a pair of a vertex \(u_{s,1}\) and a child \(u_{s',1}\) of \(u_{s,1}\) such that \(u(s,1)=u(s',1)=1\), either the edge \(a_i=(u_{s,1},u_{s',1})\) is used in the selected graph H (when \(a(i)=1\)) or a path \(P_i=(u_{s,1},v_{t',1},v_{t'+1,1},\ldots ,v_{t'',1},u_{s',1})\) from vertex \(u_{s,1}\) to vertex \(u_{s',1}\) is constructed in H with an edge \((u_{s,1},v_{t',1})\), a subpath \((v_{t',1},v_{t'+1,1},\ldots ,v_{t'',1})\) of the linkpath \(P_{t^*}\) and an edge \((v_{t'',1},u_{s',1})\) (when \(a(i)=0\)). For example, vertices \(u_{1,1}\) and \(u_{2,1}\) are connected by a path \(P_1=(u_{1,1},v_{1,1},v_{2,1},u_{2,1})\) in the selected graph \(H'\) in Fig. 5c.

(ii)
Let

\(n_\text {tree}^{\text {S}} =1+(d_\text {max}1)((d_\text {max}1)^{k^*}1)/(d_\text {max}2),\)

\(n_\text {tree}^{\text {T}} =1+(d_\text {max}2)((d_\text {max}1)^{k^*}1)/(d_\text {max}2),\)
where \(n_\text {tree}^{\text {S}} \) (resp., \(n_\text {tree}^{\text {T}}\)) is the number of vertices in the rooted tree \(T(d_\text {max}1, d_\text {max}1, k^*)\) (resp., \(T(d_\text {max}2, d_\text {max}1, k^*)\)). In each tree \(S_s\), \(s\in [1,s^*]\) (resp., \(T_t\), \(t\in [1,t^*]\)) in the scheme graph, we prepare a binary variable u(s, i) (resp., v(t, i)) for each vertex \(u_{s,i}\), \(i\in [2, n_\text {tree}^{\text {S}}]\) (resp., \(v_{t,i}\), \(i\in [2, n_\text {tree}^{\text {T}}]\)) so that \(u(s,i)=1\) (resp., \(v(t,i)=1\)) means that the corresponding vertex \(u_{s,i}\) (resp., \(v_{t,i}\)) is used as a vertex in a selected graph H. The (nonempty) subgraph of a tree \(S_s\) (resp., \(T_t\)) that consists of vertices \(u_{s,i}\) with \(u(s,i)=1\) (resp., \(v_{t,i}\) with \(v(t,i)=1\)) will be a \(k^*\)fringetree of a selected graph H.


(iii)
In the linkpath \(P_{t^*}\), we prepare a binary variable e(t), \(t\in [2,t^*]\) for each edge \(e_{t,1}=(v_{t1,1}, v_{t,1})\in E_P\) so that \(e(t)=1\) if and only if edge \(e_{t,1}\) is used in some path \(P_i=(u_{s,1},v_{t',1},v_{t'+1,1},\ldots ,v_{t'',1},u_{s',1})\) constructed in (i).

(iv)
For each pair (s, t) of \(s\in [1,s^*]\) and \(t\in [1,t^*]\), we prepare a binary variable e(s, t) (resp., e(t, s)) so that \(e(s,t')=1\) (resp., \(e(t'',s)=1\)) if and only if directed edge \((u_{s,1},v_{t',1})\) (resp., \((v_{t'',1},u_{s,1})\)) is used as the first edge (resp., last edge) of some path \(P_i=(u_{s,1},v_{t',1},v_{t'+1,1},\ldots , v_{t'',1},u_{s',1})\) constructed in (i).
Based on these, we include constraints with some more additional variables so that a selected subgraph H is a connected acyclic graph. See constraints (12) to (32) in Appendix C for the details.
In the constraints of C2, we prepare an integer variable \({\widetilde{\alpha }}(u)\) for each vertex u in the scheme graph that represents the chemical element \(\alpha (u)\in \Lambda \) if u is in a selected graph H (or \({\widetilde{\alpha }}(u)=0\) otherwise) and an integer variable \({\widetilde{\beta }}(e)\in [0,3]\) (resp., \({\widehat{\beta }}(e)\in [0,3]\)) for each edge e (resp., \(e=e(s,t)\) or e(t, s), \(s\in [1,s^*]\), \(t\in [1,t^*]\)) in the scheme graph that represents the multiplicity \(\beta (e)\in [1,3]\) if e is in a selected graph H (or \({\widetilde{\beta }}(e)\) or \({\widehat{\beta }}(e)\) takes 0 otherwise). This determines a chemical graph \(G=(H,\alpha ,\beta )\). Also we include constraints for a selected chemical graph G to satisfy the valence condition \((\alpha (u),\alpha (v),\beta (uv))\in \Gamma \) for each edge \(uv\in E\). See constraints (33) to (47) in Appendix C for the details.
In the constraints of C3, we introduce a variable for each descriptor and constraints with some more variables to compute the value of each descriptor in f(G) for a selected chemical graph G. See constraints (48) to (75) in Appendix C for the details.
Appendix C: All constraints in an MILP formulation for chemical acyclic graphs
To formulate an MILP that represents a chemical graph, we distinguish a tuple \((\mathtt{a,b},m)\) from a tuple \((\mathtt{b,a},m)\). For a tuple \(\gamma =(\mathtt{a,b},m)\in \Lambda \times \Lambda \times \{1,2,3\}\), let \(\overline{\gamma }\) denote the tuple \((\mathtt{b, a}, m)\). Let \(\Gamma _{<}\triangleq \{\overline{\gamma }\mid \gamma \in \Gamma _{>}\}\). We call a tuple \(\gamma =(\mathtt{a, b}, m) \in \Lambda \times \Lambda \times \{1, 2, 3\}\) proper if \(m \le \min \{ {\text {val}}({\mathtt{a}}), {\text {val}}(\mathtt{b})\}\) and \(m \le \max \{ {\text {val}}({\mathtt{a}}), {\text {val}}(\mathtt{b}) \}1\), where the latter is assumed because otherwise G must consist of two atoms of \(\mathtt{a=b}\). Assume that each tuple \(\gamma \in \Gamma \) is proper. Let \(\epsilon \) be a fictitious chemical element that represents null, call a tuple \((\mathtt{a,b},0)\) with \(\mathtt{a,b}\in \Lambda \cup \{\epsilon \}\) fictitious, and define \(\Gamma _0\) to be the set of all fictitious tuples; i.e., \(\Gamma _0=\{(\mathtt{a,b},0) \mid \mathtt{a,b}\in \Lambda \cup \{\epsilon \}\}\). To represent chemical elements \(\mathtt{e}\in \Lambda \cup \{\epsilon \}\cup \Gamma \) in an MILP, we encode these elements \(\mathtt{e}\) into some integers denoted by \([\mathtt{e}]\). Assume that, for each element \({\mathtt{a}}\in \Lambda \), \([{\mathtt{a}}]\) is a positive integer and that \([\epsilon ]=0\).
Upper and lower bounds on descriptors
In our formulation of an MILP for inferring a vector \(x^*\) in Stage 4, we fix the following descriptors as specified constants: the number n(G) of vertices, the diameter \(\text {dia}(G)\), and the number \({\text {bl}}_{k^*}(G)\) of leaf \(k^*\)leaf branches, which are set to be given integers \(n^*\), \(\text {dia}^*\), and \({\text {bl}}^*\), respectively. For each of the other descriptors, we specify a lower bound \(\text {LB}\) and an upper bound \(\text {UB}\) on the value so that the descriptor takes a value from the range between \(\text {LB}\) and \(\text {UB}\).
constants

\(n^*\ge 5\): the size n(G) of G;

\(\text {LB}_\text {dg}^\text {t}(i), \text {UB}_\text {dg}^\text {t}(i)\in [0,n^*],\) \(i\in [1,4], \text {t}\in \{{\text {in}},\text {ex}\}\): lower and upper bounds on the number \(\text {dg}_i^\text {t}(G)\) of \(k^*\)internal/\(k^*\)external vertices of degree i in G;

\(\text {LB}_\text {ce}^\text {t}({\mathtt{a}}), \text {UB}_\text {ce}^\text {t}({\mathtt{a}})\in [0,n^*]\), \({\mathtt{a}}\in \Lambda , \text {t}\in \{{\text {in}},\text {ex}\}\): lower and upper bounds on the number \(\text {ce}_{\mathtt{a}}^\text {t}(G)\) of \(k^*\)internal/\(k^*\)external vertices v with \(\alpha (v)={\mathtt{a}}\) in G;

\(\text {LB}_\text {bd}^\text {t}(m)\), \(\text {UB}_\text {bd}^\text {t}(m)\in [0,n^*1],\) \(m\in [2,3],\)\(\text {t}\in \{{\text {in}},\text {ex}\}\): lower and upper bounds on the number \(\text {bd}_m^\text {t}(G)\) of \(k^*\)internal/\(k^*\)external edges e with \(\beta (e)=m\) in G;

\(\text {LB}_\text {ac}^\text {t}(\gamma ),\) \(\text {UB}_\text {ac}^\text {t}(\gamma )\in [0,n^*1],\) \(\text {t}\in \{{\text {in}},\text {ex}\},\) \(\gamma \in \Gamma _{<}\cup \Gamma _{=}\): lower and upper bounds on the number \( \text {ac}_{\gamma }^\text {t}(G)\) of \(k^*\)internal/\(k^*\)external edges e with adjacencyconfiguration \(\gamma \) in G;

\(\text {LB}_\text {bc}^\text {t}(\mu ), \text {UB}_\text {bc}^\text {t}(\mu )\in [0,n^*1], \text {t}\in \{{\text {in}},\text {ex}\}\), \(\mu \in \text {Bc}\): lower and upper bounds on the number \(\text {bc}_{\mu }^\text {t}(G)\) of \(k^*\)internal/\(k^*\)external edges e with bondconfiguration \(\mu \) in G;
variables x for descriptors

\(\text {dg}^{\text {in}}(i), \text {dg}^\text {ex}(i)\in [0,n^*]\), \(i\in [1,4]\): \(\text {dg}^{\text {in}}(i)\) (resp., \(\text {dg}^\text {ex}(i)\)) represents \(\text {dg}_i^{\text {in}}(G)\) (resp., \(\text {dg}_i^\text {ex}(G)\));

\(\text {ce}^{\text {in}}({\mathtt{a}}), \text {ce}^\text {ex}({\mathtt{a}})\in [0,n^*]\), \({\mathtt{a}}\in \Lambda \): \(\text {ce}^{\text {in}}({\mathtt{a}})\) (resp., \(\text {ce}^\text {ex}({\mathtt{a}})\)) represents \(\text {ce}_{\mathtt{a}}^{\text {in}}(G)\) (resp., \(\text {ce}_{\mathtt{a}}^\text {ex}(G)\));

\(\text {bd}^{\text {in}}(m), \text {bd}^\text {ex}(m)\in [0,2n^*]\), \(m\in [1,3]\): \(\text {bd}^{\text {in}}(m)\) (resp., \(\text {bd}^\text {ex}(m)\)) represents \(\text {bd}_m^{\text {in}}(G)\) (resp., \(\text {bd}_m^\text {ex}(G)\));

\(\text {ac}^{\text {in}}(\gamma ), \text {ac}^\text {ex}(\gamma )\in [0,n^*]\), \(\gamma \in \Gamma _{<}\cup \Gamma _{=}\): \(\text {ac}^{\text {in}}(\gamma )\) (resp., \(\text {ac}^\text {ex}(\gamma )\)) represents \( \text {ac}_{\gamma }^{\text {in}}(G)\) (resp., \( \text {ac}_{\gamma }^\text {ex}(G)\));

\(\text {bc}^{\text {in}}(\mu ), \text {bc}^\text {ex}(\mu )\in [0,n^*1]\), \(\mu \in \text {Bc}\): \(\text {bc}^{\text {in}}(\mu )\) (resp., \(\text {bc}^\text {ex}(\mu )\)) represents \(\text {bc}_{\mu }^{\text {in}}(G)\) (resp., \(\text {bc}_{\mu }^\text {ex}(G)\));
constraints
We use the rangebased method to define an applicability domain for our method. For this, we find the range (the minimum and maximum) of each descriptor over all relevant chemical compounds and represent each range as a set of linear constraints in the constraint set \({\mathcal {C}}_1\) of our MILP formulation. Recall that \(D_{\pi }\) stands for a set of chemical graphs used for constructing a prediction function. However, the number of examples in \(D_{\pi }\) may not be large enough to capture a general feature on the structure of chemical graphs. For this, we also use some data set from the whole set \(\text {DB}\) of chemical graphs in a database. Let \(\text {DB}_{{\mathcal {G}}}^{(i)}\) denote the set of chemical graphs \(G\in \text {DB}\cap {\mathcal {G}}\) such that \(n(G)=i\) for each integer \(i\ge 1\). Based on this, we assume that the given lower and upper bounds on the above descriptors satisfy the following. For each \(\text {t}\in \{{\text {in}},\text {ex}\}\),
Construction of scheme graph
We infer a subgraph H such that the maximum degree is \(d_\text {max}\in \{3,4\}\), \(n(H)=n^*\), \(\text {bh}_{k^*}(H)=\text {bh}^*\), and \({\text {bl}}_{k^*}(H)={\text {bl}}^*\). For this, we first construct the scheme graph \(\text {SG}( d_\text {max}, k^*, \text {bh}^*, t^*)\). We then prepare a binary variable u(s, i) (resp., v(t, i)) for each vertex \(u_{s,i}\) in tree \(S_s\) (resp., \(v_{t,i}\) in tree \(T_t\)).
Recall that when the two endvertices of edge \(a_i=(u_{s,1},u_{s',1}) \in E_B=\{a_1,a_2,\ldots ,a_{c^*}\}\) is connected in a selected subgraph H, either edge \(a_i\) is directly used in H or a path \(P_i=(u_{s,1},v_{t',1},v_{t'+1,1},\ldots ,\) \(v_{t'',1},u_{s',1})\) from \(u_{s,1}\) to \(u_{s',1}\) visiting some vertices in \(P_{t^*}\) is constructed in H. We regard the index i of each edge \(a_i\in E_B=\{a_1,a_2,\ldots ,a_{c^*}\}\) as the “color” of the edge, and define the color set of \(E_B\) to be \([1,c^*]\). To introduce necessary linear constraints that can construct such a path \(P_i\) properly in our MILP, we assign the color i to the vertices \(v_{t',1},v_{t'+1,1},\ldots ,\) \(v_{t'',1}\) in \(P_{t^*}\) when a path \(P_i=(u_{s,1},v_{t',1},v_{t'+1,1},\ldots ,\) \(v_{t'',1},u_{s',1})\) is used in H.
constants
Integers \(d_\text {max}\in \{3,4\}\), \(n^*\ge 3\), \(\text {dia}^*\ge 3\), \(k^*\ge 1\), \(\text {bh}^*\ge 1\) and \({\text {bl}}^*\ge 2\);
variables

\(a(i)\in \{0,1\}\), \(i\in E_B\): a(i) represents edge \(a_i\in E_B\) (\(a(i)=1\), \(i\in E_B\)) (\(a(i)=1\) \(\Leftrightarrow \) edge \(a_i\) is used in H);

\(e(s,t),e(t,s)\in \{0,1\}\), \(s\in [1,s^*]\), \(t\in [1,t^*]\): e(s, t) (resp., e(t, s)) represents direction \((u_{s,1}, v_{t,1})\) (resp., \((v_{t,1}, u_{s,1})\)), where \(e(s,t)=1\) (resp., \(e(t,s)=1\)) \(\Leftrightarrow \) edge \(u_{s,1},v_{t,1}\) is used in H and direction \((u_{s,1}, v_{t,1})\) (resp., \((v_{t,1}, u_{s,1})\)) is assigned to edge \(u_{s,1} v_{t,1}\);

\(\chi (t)\in [0,c^*]\), \(t\in [1,t^*]\): \(\chi (t)\) represents the color \(c\in [0,c^*]\) assigned to vertex \(v_{t,1}\) (\(\chi (t)=c\) \(\Leftrightarrow \) vertex \(v_{t,1}\) is assigned color c, where \(\chi (t)=c=0\) iff \(v_{t,1}\) is not in H);

\(\delta _{\text {clr}}(t,c)\in \{0,1\}\), \(t\in [1,t^*]\), \(c\in [0,c^*]\) (\(\delta _{\text {clr}}(t,c)=1\) \(\Leftrightarrow \) \(\chi (t)=c\));

\(\text {clr}(c)\in [0,t^*]\), \(c\in [0,c^*]\): the number of vertices \(v_{t,i}\) with color c;

\(\deg ^\text {b+}(s)\in [0,4]\), \(s\in [1,s^*]\): the outdegree of vertex \(u_{s,1}\) in the \(k^*\)branchsubtree of H;

\(\deg ^\text {b}(s)\in [0,4]\), \(s\in [1,s^*]\): the indegree of vertex \(u_{s,1}\) in the \(k^*\)branchsubtree of H;
constraints
Selecting a subgraph
From the scheme graph \(\text {SG}( d_\text {max}, k^*, \text {bh}^*, t^*)\), we select a subgraph H such that \(n(H)=n^*\), \(\text {dia}(H)=\text {dia}^*\), \(\text {bh}_{k^*}(H)=\text {bh}^*\), and \({\text {bl}}_{k^*}(H)={\text {bl}}^*\).
constants

Integers \(d_\text {max}\in \{3,4\}\), \(n^*\ge 3\), \(\text {dia}^*\ge 3\), \(k^*\ge 1\), \(\text {bh}^*\ge 1\) and \({\text {bl}}^*\ge 2\);

For each tree \(S_s=T(d_\text {max}1, d_\text {max}1, k^*)\), prepare

the set \(\text {Cld}_{\text {S}}(i)\) of the indices of children of a vertex \(v_i\);

the index \(\text {prt}(i)\) of the parent of a nonroot vertex \(v_i\);

the set \(\text {Dsn}_S(d)\) of indices i of a vertex \(v_i\) whose depth is d;

a proper set \(P_{\text {prc}}(d_\text {max}1, d_\text {max}1, k^*)\) of index pairs,

where we denote \(P_{\text {prc}}(d_\text {max}1, d_\text {max}1, k^*)\) by \(P_{S,\text {prc}}\);


For each tree \(T_t=T(d_\text {max}2, d_\text {max}1, k^*)\), prepare

the set \(\text {Cld}_{\text {T}}(i)\) of the indices of children of a vertex \(v_i\);

the index \(\text {prt}(i)\) of the parent of a nonroot vertex \(v_i\);

a proper set \(P_{\text {prc}}(d_\text {max}2, d_\text {max}1, k^*)\) of index pairs,

where we denote \(P_{\text {prc}}(d_\text {max}2, d_\text {max}1, k^*)\) by \(P_{T,\text {prc}}\);

variables

\(\sigma (s)\in \{0,1\}\), \(s\in [1,s^* ]\): (\(\sigma (s)=1\) \(\Leftrightarrow \) vertex \(u_{s,1}\) is a nonleaf \(k^*\)branch or a root);

\(u(s,i)\in \{0,1\}\), \(s\in [1,s^* ]\), \(i\in [1,n_\text {tree}^{\text {S}}]\): u(s, i) represents vertex \(u_{s,i}\) (\(u(s,i)=1\) \(\Leftrightarrow \) vertex \(u_{s,i}\) is used in H and edge \(e'_{s,i}\) \((i\ge 2)\) is used in H), (\(u(s,1)=1\) and \(\sigma (s)=0\) \(\Leftrightarrow \) vertex \(u_{s,1}\) is a leaf \(k^*\)branch);

\(v(t,i)\in \{0,1\}\), \(t\in [1,t^* ]\), \(i\in [1,n_\text {tree}^{\text {T}}]\): v(t, i) represents vertex \(v_{t,i}\) (\(v(t,i)=1\) \(\Leftrightarrow \) vertex \(v_{t,i}\) is used in H and edge \(e_{t,i}\) \((i\ge 2)\) is used in H);

\(e(t)\in \{0,1\}\), \(t\in [1,t^*+1]\): e(t) represents edge \(e_{t,1}=v_{t1,1} v_{t,1}\), where \(e_{1,1}\) and \(e_{t^*+1,1}\) are fictitious edges (\(e(t)=1\) \(\Leftrightarrow \) edge \(e_{t,1}\) is used in H);
constraints
Constraints (21) and (22) represent an extension of constraint (1) on the size of 2fringetrees to the case of a general branchparameter \(k^*\).
Assigning multiplicity
We prepare an integer variable \({\widetilde{\beta }}(e)\) or \({\widehat{\beta }}(e)\) for each edge e in the scheme graph \(\text {SG}( d_\text {max}, k^*, \text {bh}^*, t^*)\) to denote the multiplicity of e in a selected graph H and include necessary constraints for the variables to satisfy in H.
constants

Prepare functions \(\text {tail}\) and \(\text {head}\) such that \(a_{i}=(u_{\text {tail}(i)}, u_{\text {head}(i)})\in E_B\);

Assume that each edge in a tree \(S_s\), \(s\in [1,s^*]\) (resp., \(T_t\), \(t\in [1,t^*]\)) is denoted by \(e'_{s,i}\) (resp., \(e_{t,i}\)) with the integer \(i\in [2,n_\text {tree}^{\text {S}}]\) of the head \(u_{s,i}\) (resp., \(v_{t,i}\)) of the edge;
variables

\({\widetilde{\beta }}(i)\in [0,3]\), \(i\in [1,c^*]\): \({\widetilde{\beta }}(i)\) represents the multiplicity of edge \(a_{i}\), where \({\widetilde{\beta }}(i)=0\) if edge \(a_{i}\) is not in an inferred chemical graph G;

\({\widetilde{\beta }}(p,i)\in [0,3]\), \(p\in [1,s^*+ t^*]\), \(i\in [2,n_\text {tree}^{\text {S}}]\): \({\widetilde{\beta }}(p,i)\) with \(p\le s^*\) (resp., \(p>s^*\)) represents the multiplicity of edge \(e'_{p,i}\) (resp., \(e_{ps^*,i}\));

\({\widetilde{\beta }}(t,1)\in [0,3]\), \(t\in [1, t^*+1]\): \({\widetilde{\beta }}(t,1)\) represents the multiplicity of edge \(e_{t,1}\);

\({\widehat{\beta }}(s,t)\in [0,3]\), \(s\in [1,s^*]\), \(t\in [1,t^*]\): \({\widehat{\beta }}(s,t)\) represents the multiplicity of edge \(u_{s,1}v_{t,1}\);
constraints
Assigning chemical elements and valence condition
We include constraints so that each vertex v in a selected graph H satisfies the valence condition; i.e., \(\beta (v)\le {\text {val}}(\alpha (v))\). With these constraints, a chemical acyclic graph \(G=(H,\alpha ,\beta )\) on a selected subgraph H will be constructed.
constants

A set \(\Lambda \cup \{\epsilon \}\) of chemical elements, where \(\epsilon \) denotes null;

A coding \([{\mathtt{a}}]\), \({\mathtt{a}}\in \Lambda \cup \{\epsilon \}\) such that \([\epsilon ]=0\); \([{\mathtt{a}}]\ge 1\), \({\mathtt{a}}\in \Lambda \); and \([{\mathtt{a}}]\ne [\mathtt{b}]\) if \({\mathtt{a}}\ne \mathtt{b}\); Let \([\Lambda ]\) and \([\Lambda \cup \{\epsilon \}]\) denote \(\{[{\mathtt{a}}]\mid {\mathtt{a}}\in \Lambda \}\) and \(\{[{\mathtt{a}}]\mid {\mathtt{a}}\in \Lambda \cup \{\epsilon \}\}\), respectively;

A valence function: \({\text {val}}: \Lambda \rightarrow [1,4]\);

Let \(E_B(s)\) denote the set of indices i of all edges \(a_i\in E_B\) adjacent to vertex \(u_{s,1}\) in \(T_B\).
variables

\({\widetilde{\alpha }}(p,i)\in [\Lambda \cup \{\epsilon \}]\), \(p\in [1,s^*+ t^*]\), \(i\in [1,n_\text {tree}^{\text {S}}]\): \({\widetilde{\alpha }}(p,i)\) with \(p\le s^*\) (resp., \(p>s^*\)) represents \(\alpha (u_{p,i})\) (resp., \(\alpha (v_{ps^*,i})\));

\(\delta _{\alpha }(p,i,{\mathtt{a}})\in \{0,1\}\), \(p\in [1,s^*+ t^*]\), \(i\in [1,n_\text {tree}^{\text {S}}]\), \({\mathtt{a}}\in \Lambda \cup \{\epsilon \}\): \(\delta _{\alpha }(p,i,{\mathtt{a}})=1\) \(\Leftrightarrow \) \(\alpha (u_{p,i})={\mathtt{a}}\) for \(p\le s^*\) and \(\alpha (v_{ps^*,i})={\mathtt{a}}\) for \(p> s^*\);

\(\delta _{{\widetilde{\beta }}}(i,m)\in \{0,1\}\), \(p\in [1,s^*+ t^*]\), \(i\in [1,c^*]\), \(m\in [0,3]\): \(\delta _{{\widetilde{\beta }}}(i,m)=1\) \(\Leftrightarrow \) the multiplicity of edge \(a_i\) in an inferred chemical graph G is m;

\(\delta _{{\widetilde{\beta }}}(p,i,m)\in \{0,1\}\), \(p\in [1,s^*+ t^*]\), \(i\in [2,n_\text {tree}^{\text {S}}]\), \(m\in [0,3]\): \(\delta _{{\widetilde{\beta }}}(p,i,m)=1\) \(\Leftrightarrow \) the multiplicity of edge \(e'_{p,i}\), \(p\le s^*\) (or \(e_{ps^*,i}\), \(p>s^*\)) in G is m;

\(\delta _{{\widetilde{\beta }}}(t,1,m)\in \{0,1\}\), \(t\in [1, t^*+1]\), \(m\in [0,3]\): \(\delta _{{\widetilde{\beta }}}(t,1,m)=1\) \(\Leftrightarrow \) the multiplicity of edge \(e_{t}\) in G is q;

\(\delta _{{\widehat{\beta }}}(s,t,m)\in \{0,1\}\), \(s\in [1,s^*]\), \(t\in [1,t^*]\), \(m\in [0,3]\): \(\delta _{{\widehat{\beta }}}(s,t,m)=1\) \(\Leftrightarrow \) the multiplicity of edge \(u_{s,1}v_{t,1}\) in G is m;
constraints
Descriptors on mass, the numbers of elements and bonds
We include constraints to compute descriptors \(\overline{\text {ms}}(G)\), \(\text {ce}_{\mathtt{a}}(G)\) (\({\mathtt{a}}\in \Lambda )\), \(\text {bd}_m(G)\) (\(m\in [2,3]\)) and \(n_\mathtt{H}(G)\) according to the definitions in "Modeling of chemical compounds" section.
constants

A function \(\text {mass}^*:\Lambda \rightarrow {\mathbb {Z}}\) (we let \(\text {mass}({\mathtt{a}})\) denote the observed mass of a chemical element \({\mathtt{a}}\in \Lambda \), and define \(\text {mass}^*({\mathtt{a}})=\lfloor 10\cdot \text {mass}({\mathtt{a}})\rfloor \));
variables

\(\text {Mass}\in {\mathbb {Z}}\): \(\text {Mass}\) represents \(\sum _{v\in V} \text {mass}^*(\alpha (v))\);

\(\text {bd}(m)\in [0,2n^*]\), \(m\in [1,3]\);

\(\text {n}_\mathtt{H}\in [0,4n^*]\): the number \(n_\mathtt{H}(G)\) of hydrogen atoms to be included to G;
constraints
Descriptor for the Number of Specified Degree
We include constraints to compute descriptors \(\text {dg}_i(G)\) (\(i\in [1,4]\)) according to the definitions in "Modeling of chemical compounds" section. We also add constraints so that the maximum degree of a vertex in H is at most 3 (resp., equal to 4) when \(d_\text {max}=3\) (resp., \(d_\text {max}=4)\).
variables

\(\deg (p,i)\in [0,4]\), \(p\in [1,s^*+ t^*]\), \(i\in [1,n_\text {tree}^{\text {S}}]\): \(\deg (p,i)\) represents \(\deg _H(u_{p,i})\) for \(p\le s^*\) or \(\deg _H(v_{ps^*,i})\) for \(p> s^*\);

\(\delta _{\deg }(p,i,d)\in \{0,1\}\), \(p\in [1,s^*+ t^*]\), \(i\in [1,n_\text {tree}^{\text {S}}]\), \(d\in [0,4]\): \(\delta _{\deg }(p,i,d)=1\) \(\Leftrightarrow \) \(\deg (p,i)=d\);
constraints
Descriptor for the number of adjacencyconfigurations
We include constraints to compute descriptors \(\text {ac}_{\gamma }(G)\) (\(\gamma =(\mathtt{a,b},m)\in \Gamma \)) according to the definitions in "Modeling of chemical compounds" section.
constants

A set \(\Gamma =\Gamma _{<}\cup \Gamma _{=}\cup \Gamma _{>}\) of proper tuples \((\mathtt{a,b},m)\in \Lambda \times \Lambda \times [1,3]\);

The set \(\Gamma _0=\{(\mathtt{a,b},0)\mid \mathtt{a,b}\in \Lambda \cup \{\epsilon \}\}\);
variables

\(\delta _{\tau }(i,\gamma )\in \{0,1\}\), \(i\in [1,c^*]\), \(\gamma \in \Gamma \cup \Gamma _0 \): \(\delta _{\tau }(i, \gamma )=1\) \(\Leftrightarrow \) edge \(a_{i}\) is assigned tuple \(\gamma \); i.e., \(\gamma =({\widetilde{\alpha }}(\text {tail}(i),1), {\widetilde{\alpha }}(\text {head}(i),1), {\widetilde{\beta }}(i))\);

\(\delta _{\tau }(t,1,\gamma )\in \{0,1\}\), \(t\in [2,t^*]\), \(\gamma \in \Gamma \cup \Gamma _0 \): \(\delta _{\tau }(t,1, \gamma )=1\) \(\Leftrightarrow \) edge \(e_{t,1}\) is assigned tuple \(\gamma \); i.e., \(\gamma =({\widetilde{\alpha }}(s^*+ t1,1), {\widetilde{\alpha }}(s^*+ t,1), {\widetilde{\beta }}(t,1))\);

\(\delta _{\tau }(p,i,\gamma )\in \{0,1\}\), \(p\in [1,s^*+ t^*]\), \(i\in [2,n_\text {tree}^{\text {S}}]\), \(\gamma \in \Gamma \cup \Gamma _0 \): \(\delta _{\tau }(p,i, \gamma )=1\) \(\Leftrightarrow \) edge \(e'_{p,i}\), \(p\le s^*\) (or \(e_{ps^*,i}\), \(p> s^*\)) is assigned tuple \(\gamma \); i.e., \(\gamma =({\widetilde{\alpha }}(p,\text {prt}(i)), {\widetilde{\alpha }}(p,i), {\widetilde{\beta }}(p,i))\);

\(\delta _{{\widehat{\tau }}}(s,t,\gamma )\in \{0,1\}\), \(s\in [1,s^*]\), \(t\in [1,t^*]\), \(\gamma \in \Gamma \cup \Gamma _0 \): \(\delta _{{\widehat{\tau }}}(s,t, \gamma )=1\) \(\Leftrightarrow \) edge \(u_{s,1}v_{t,1}\) is assigned tuple \(\gamma \); i.e., \(\gamma =({\widetilde{\alpha }}(s,1), {\widetilde{\alpha }}(s^*+ t,1), {\widehat{\beta }}(s,t))\);
constraints
Descriptor for bondconfiguration
We include constraints to compute the descriptors for bondconfiguration \(\text {bd}_{\mu }(G)\), \(\mu \in \text {Bc}\), according to the definition.
variables

\(\text {bc}(\mu )\in [0,n^*1]\), \(\mu \in \text {Bc}\);

\(\delta _{\text {dc}}(i,d,d',m)\in \{0,1\}\), \(i\in [1,c^*]\), \(d,d'\in [0, 4]\), \(m\in [0,3]\): \(\delta _{\text {dc}}(i,d,d',m)=1\) \(\Leftrightarrow \) \(\deg _H(u_{{\text {tail}}(i)})=d\), \(\deg _H(u_{{\text {head}}(i)})=d'\) and \(\beta (a_i)=m\in [1,3]\) in G;

\(\delta _{\text {dc}}(t,1,d,d',m)\in \{0,1\}\), \(t\in [2,t^*]\), \(d,d'\in [0, 4]\), \(m\in [0,3]\): \(\delta _{\text {dc}}(t,1,d,d',m)=1\) \(\Leftrightarrow \) \(\deg _H(v_{t1,1})=d\), \(\deg _H(v_{t,1})=d'\) and \(\beta (e_{t,1})=m\in [1,3]\) in G;

\(\delta _{\text {dc}}(p,i,d,d',m)\in \{0,1\}\), \(p\in [1,s^*+ t^*]\), \(i\in [2,n_\text {tree}^{\text {S}}]\), \(d,d'\in [0, 4]\), \(m\in [0,3]\): \(\delta _{\text {dc}}(p,i,d,d',m)=1\) \(\Leftrightarrow \) \(\deg _H(u_{p,\text {prt}(i)})=d\), \(\deg _H(u_{p,i})=d'\) and \(\beta (e'_{p,i})=m\in [1,3]\) for \(p\le s^*\) (or \(\deg _H(v_{ps^*,\text {prt}(i)})=d\), \(\deg _H(v_{ps^*,i})=d'\) and \(\beta (e_{ps^*,i})=m\in [1,3]\) for \(p> s^*\)) in G;

\(\delta _{\widehat{\text {dc}}}(s,t,d,d',m)\in \{0,1\}\), \(s\in [1,s^*]\), \(t\in [1,t^*]\), \(d,d'\in [0, 4]\), \(m\in [0,3]\): \(\delta _{\widehat{\text {dc}}}(s,t,d,d',1)=1\) \(\Leftrightarrow \) \(\deg _H(u_{s,1})=d\), \(\deg _H(v_{t,1})=d'\) and \(\beta (u_{s,1}v_{t,1})=m\in [1,3]\) in G;
constraints
Appendix D: Descriptions of new graph search algorithms
Multirooted trees and frequency vectors
For a finite set A of elements, let \({\mathbb {Z}}_+^A\) denote the set of functions \(\pmb {w}:A\rightarrow {\mathbb {Z}}_+\). A function \(\pmb {w}\in {\mathbb {Z}}_+^A\) is called a nonnegative integer vector (or a vector) on A and the value \(\pmb {x}(a)\) for an element \(a\in A\) is called the entry of \(\pmb {x}\) for \(a\in A\). For a vector \(\pmb {w}\in {\mathbb {Z}}_+^A\) and an element \(a\in A\), let \(\pmb {w}+\pmb {1}_{a}\) (resp., \(\pmb {w}\pmb {1}_{a}\)) denote the vector \(\pmb {w}'\) such that \(\pmb {w}'(a)=\pmb {w}(a)+1\) (resp., \(\pmb {w}'(a)=\pmb {w}(a)1\)) and \(\pmb {w}'(b)=\pmb {w}(b)\) for the other elements \(b\in A\setminus \{a\}\). For a vector \(\pmb {w}\in {\mathbb {Z}}_+^A\) and a subset \(B\subseteq A\), let \(\pmb {w}_{[B]}\) denote the projection of \(\pmb {w}\) to B; i.e., \(\pmb {w}_{[B]}\in {\mathbb {Z}}_+^B\) such that \(\pmb {w}_{[B]}(b)=\pmb {w}(b)\), \(b\in B\).
Let \(\text {Bc}\) denote the set of tuples \(\mu =(d_1,d_2,k)\in [1,4]\times [1,4]\times [1,3]\) (bondconfiguration) such that \(\max \{d_1,d_2\}+k\le 4\). For two tuples \(\mu =(d_1,d_2,k), \mu '=(d'_1,d'_2,k')\in \text {Bc}\), we write \(\mu \ge \mu '\) if

\(\max \{d_1,d_2\}\ge \max \{d'_1,d'_2\}\), \(\min \{d_1,d_2\}\ge \min \{d'_1,d'_2\}\) and \(k\ge k'\),
and write \(\mu > \mu '\) if

\(\mu \ge \mu '\) and \(\mu \ne \mu '\).
Let \(\text {Dg}=\{\text {dg}1, \text {dg}2, \text {dg}3, \text {dg}4\}\), where \(\text {dg}i\) denotes the number of vertices with degree i.
Henceforth we deal with vectors \(\pmb {w}\) that have their \(\pmb {w}_{\text {in}}\) and \(\pmb {w}_{\text {ex}}\) components, both \(\pmb {w}_{{\text {in}}}, \pmb {w}_{\text {ex}}\in {\mathbb {Z}}_+^{ {\Lambda \cup \Gamma \cup {\text {Bc}}\cup {\text {Dg}}}}\), and for convenience we write \(\pmb {w}= (\pmb {w}_{\text {in}}, \pmb {w}_\text {ex})\) in the sense of concatenation.
For a vector \(\pmb {x}= (\pmb {x}_{\text {in}}, \pmb {x}_\text {ex})\) with \(\pmb {x}_{\text {in}}, \pmb {x}_\text {ex}\in {\mathbb {Z}}_+^{ {\Lambda \cup \Gamma \cup \text {Bc}\cup \text {Dg}}}\), let \({\mathcal {G}}(\pmb {x})\) denote the set of chemical acyclic graphs G whose 2internal (resp., 2external) vertices/edges are determined by the vector \(\pmb {x}_{\text {in}}\) (resp., \(\pmb {x}_\text {ex}\)); i.e., G satisfies the following:

\(\text {ce}_{\mathtt{a}}^{\text {in}}(G)=\pmb {x}_{\text {in}}({\mathtt{a}})\) and \(\text {ce}_{\mathtt{a}}^\text {ex}(G)=\pmb {x}_\text {ex}({\mathtt{a}})\) for each chemical element \({\mathtt{a}}\in \Lambda \),

\(\text {ac}_{\gamma }^{\text {in}}(G)= \pmb {x}_{\text {in}}(\gamma )\) and \(\text {ac}_{\gamma }^\text {ex}(G)=\pmb {x}_\text {ex}(\gamma )\) for each adjacencyconfiguration \(\gamma \in \Gamma \),

\(\text {bc}_{\mu }^{\text {in}}(G)=\pmb {x}_{\text {in}}(\mu )\) and \(\text {bc}_{\mu }^\text {ex}(G)=\pmb {x}_\text {ex}(\mu )\) for each bondconfiguration \(\mu \in \text {Bc}\),

\(\text {dg}_i^{\text {in}}(G)=\pmb {x}_{\text {in}}(\text {dg}i)\) and \(\text {dg}_i^\text {ex}(G)=\pmb {x}_\text {ex}(\text {dg}i)\) for each degree \(\text {dg}i\in \text {Dg}\).
Throughout the section, let \(k^*=2\) be a branchparameter, \(\pmb {x}^*= (\pmb {x}_{\text {in}}^*, \pmb {x}_\text {ex}^*)\) be a given feature vector with \(\pmb {x}_{\text {in}}^*, \pmb {x}_\text {ex}^*\in {\mathbb {Z}}_+^ {\Lambda \cup \Gamma \cup \text {Bc}\cup \text {Dg}}\), and \(\text {dia}^*\) be an integer. We infer a chemical acyclic graph \(G\in {\mathcal {G}}(\pmb {x}^*)\) such that \({\text {bl}}_2(G)\in [2,3]\) and the diameter of G is \(\text {dia}^*\), where \(n^*=\sum _{{\mathtt{a}}\in \Lambda }(\pmb {x}^*_{\text {in}}({\mathtt{a}})+\pmb {x}^*_\text {ex}({\mathtt{a}}) )\). Note that any other descriptors of \(G\in {\mathcal {G}}(\pmb {x}^*)\) can be determined by the entries of vector \(\pmb {x}^*\).
To infer a chemical acyclic graph \(G\in {\mathcal {G}}(\pmb {x}^*)\), we consider a connected subgraph T of G that consists of
Our method first generates a set \(\text {FT}\) of all possible rooted trees T that can be a 2fringetree of a chemical graph \(G\in {\mathcal {G}}(\pmb {x}^*)\), and then extends the trees T by repeatedly appending a tree in \(\text {FT}\) until a chemical graph \(G\in {\mathcal {G}}(\pmb {x}^*)\) is formed. In the extension, we actually manipulate the “frequency vectors” of trees defined below.
To specify which part of a given tree T plays the role of 2internal vertices/edges or 2external vertices/edges in a chemical graph \(G\in {\mathcal {G}}(\pmb {x}^*)\) to be inferred, we designate at most three vertices \(r_1(T)\), \(r_2(T)\), and \(r_3(T)\), in T as terminals, and call T rooted (resp., birooted and trirooted) if the number of terminals is one (resp., two and three). For a rooted tree (resp., bi or trirooted tree) T, let \({\widetilde{V}}_{{\text {in}}}\) denote the set of vertices contained in a path between two terminals of T, \({\widetilde{E}}_{{\text {in}}}\) denote the set of edges in T between two vertices in \({\widetilde{V}}_{{\text {in}}}\), and define \({\widetilde{V}}_{\text {ex}}\triangleq V(T)\setminus {\widetilde{V}}_{{\text {in}}}\) and \({\widetilde{E}}_{\text {ex}}\triangleq E(T)\setminus {\widetilde{E}}_{{\text {in}}}\). For a bi or trirooted tree T, define the backbone path \(P_T\) of T to be the path of T between vertices \(r_1(T)\) and \(r_2(T)\).
Given a chemical acyclic graph T, define \(\pmb {f}_\mathtt{t}(T)\), \(\mathtt{t}\in \{{\text {in}},\text {ex}\}\), to be the vector \(\pmb {w}\in {\mathbb {Z}}_+^{\Lambda \cup \Gamma \cup \text {Bc}\cup \text {Dg}}\) that consists of the following entries:

\(\pmb {w}({\mathtt{a}})=\{v\in {\widetilde{V}}_\mathtt{t} \mid \alpha (v)={\mathtt{a}}\}\), \({\mathtt{a}}\in \Lambda \),

\(\pmb {w}(\gamma )=\{uv\in {\widetilde{E}}_\mathtt{t} \mid \{\alpha (u),\alpha (v)\}=\{\mathtt{a,b}\}, \beta (uv)=q\}\), \(\gamma =(\mathtt{a,b}, q)\in \Gamma \),

\(\pmb {w}(\mu )=\{uv\in {\widetilde{E}}_\mathtt{t}\mid \{\deg _T(u),\deg _T(v)\}=\{ d,d'\}, \beta (uv)=m\}\), \(\mu =(d,d', m)\in \text {Bc}\),

\(\pmb {w}(\text {dg}i)=\{v\in {\widetilde{V}}_\mathtt{t}\mid \deg _T(v)=i\}\), \(\text {dg}i\in \text {Dg}\).
Define \(\pmb {f}(T)\triangleq (\pmb {f}_{\text {in}}(T),\pmb {f}_\text {ex}(T))\). The entry for an element \(\mathtt{e}\in {\Lambda \cup \Gamma \cup \text {Bc}\cup \text {Dg}}\) in \(\pmb {f}_\mathtt{t}(T)\), \(\mathtt{t}\in \{{\text {in}},\text {ex}\}\) is denoted by \(\pmb {f}_\mathtt{t}(\mathtt{e}; T)\). For a subset B of \( {\Lambda \cup \Gamma \cup \text {Bc}\cup \text {Dg}}\), let \(\pmb {f}_{\mathtt{t}[B]}(T)\) denote the projection of \(\pmb {f}_\mathtt{t}(T)\) onto B.
Our aim is to generate all chemical birooted (resp., trirooted) trees T with diameter \(\text {dia}^*\) such that \(\pmb {f}(T)=\pmb {x}^*\).
A new algorithm for computing chemical birooted trees G with \({\text {bl}}_2(G)=2\)
This section describes a sketch of our new graph search algorithm for the case of \({\text {bl}}_2(G)=2\). See Appendix “A sketch of algorithm for computing chemical trirooted trees G with \({\text {bl}}_2(G)=3\)” for a sketch of a new algorithm for the case of \({\text {bl}}_2(G)=3\).
We call a chemical graph \(G\in {\mathcal {G}}(\pmb {x}^*)\) with diameter \(\text {dia}^*\) and \({\text {bl}}_2(G)=2\) a target graph.
A chemical acyclic graph G with \({\text {bl}}_2(G)=2\) has exactly two leaf 2branches \(v_i\), \(i=1,2\), where the length of the path between the two leaf 2branches \(v_1\) and \(v_2\) of a target graph G is \(\text {dia}^*2k^*=\text {dia}^*4\). We observe that a connected subgraph T of a target graph G that satisfies (76) for \({\text {bl}}_2(G)=2\) is a chemical rooted or birooted tree with roots u and v, where possibly \(u = v\). We call such a subgraph T an internalsubtree (resp., endsubtree) of G if neither (resp., one) of u and v is a 2branch in G. When \(u=v\), we call an internalsubtree (resp., endsubtree) T of G an internalfringetree (resp., endfringetree) of G. Figure 10a–d illustrates an internalsubtree, an internalfringetree, an endsubtree and an endfringetree of G.
Let \(\delta _1=\lfloor \frac{\text {dia}^*  5}{2} \rfloor \) and \(\delta _2 =\text {dia}^*5\delta _1=\lceil \frac{\text {dia}^*  5}{2} \rceil \). We regard a target graph \(G\in {\mathcal {G}}(\pmb {x}^*)\) with \({\text {bl}}_2(G)=2\) and diameter \(\text {dia}^*\) as a combination of two chemical birooted trees \(T_1\) and \(T_2\) with \(\ell (P_{T_i})=\delta _i\), \(i=1,2\), joined by an edge \(e=r_1(T_1)r_1(T_2)\), as illustrated in Fig. 11.
We start with generating chemical rooted trees and then iteratively extend chemical birooted trees T with \(\ell (P_T)=1,2,\dots ,\delta _1\), before we finally combine two chemical birooted trees \(T_1\) and \(T_2\) with \(\ell (P_{T_i})=\delta _i\). To describe our algorithm, we introduce some notation.

Let \({\mathcal {T}}(\pmb {x}^*)\) denote the set of all birooted trees T (where possibly \(r_1(T)=r_2(T)\)) such that \(\pmb {f}_{{\text {in}}}(T)\le \pmb {x}_{{\text {in}}}^*\) and \(\pmb {f}_{\text {ex}}(T)\le \pmb {x}_{\text {ex}}^*\), which is a necessary condition for T to be an internalsubtree or endsubtree of a target graph \(G\in {\mathcal {G}}(\pmb {x}^*)\).

Let \({\mathcal {FT}}\) denote the set of all rooted trees \(T\in {\mathcal {T}}(\pmb {x}^*)\) that can be a 2fringetree of a target graph G, where T satisfies the size constraint (1) of 2fringetrees.

For each integer \(h\in [1,\text {dia}^*4]\), let \({\mathcal {T}}_{\text {end}}^{(h)}\) denote the set of all birooted trees \(T\in {\mathcal {T}}(\pmb {x}^*)\) that can be an endsubtree of a target graph G such that \(\ell (P_T)=h\), and each 2fringetree \(T_v\) rooted at a vertex v in \(P_T\) belongs to \({\mathcal {FT}}\).
The idea of our new algorithm is to compute only the set \(\text {W}_{\text {end}}^{(h)}\) of frequency vectors \(\pmb {w}\) of end trees, whose size \(\text {W}_{\text {end}}^{(h)}\) is much more restricted than that of \({\mathcal {T}}_{\text {end}}^{(h)}\). We compute the set \(\text {W}_{\text {end}}^{(h)}\) of frequency vectors \(\pmb {w}\) of trees in \({\mathcal {T}}_{\text {end}}^{(h)}\) iteratively for each integer \(h\ge 0\). During the computation, we keep a sample of a tree \(T_{\pmb {w}}\) for each frequency vector \(\pmb {w}\) so that a final step can construct some number of target graphs G by assembling these sample trees. Based on this, we generate target graphs \(G\in {\mathcal {G}}(\pmb {x}^*)\) by the following steps:

1.

(i)
Compute \({\mathcal {FT}}\) by a branchandbound procedure that generates all possible rooted trees \(T\in {\mathcal {T}}(\pmb {x}^*)\) (where \(r_1(T)=r_2(T)\)) that can be a 2fringetree of a target graph \(G\in {\mathcal {G}}(\pmb {x}^*)\);

(ii)
Compute the set \(\text {W}^{(0)}\) of all vectors \(\pmb {w}=(\pmb {w}_{\text {in}},\pmb {w}_\text {ex})\) such that \(\pmb {w}_{\text {in}}=\pmb {f}_{{\text {in}}}(T)\) and \(\pmb {w}_\text {ex}=\pmb {f}_{\text {ex}}(T)\) for some tree \(T\in {\mathcal {FT}}\), and let \(\text {W}_{\text {end}}^{(0)} \subseteq \text {W}^{(0)}\) be those trees with height exactly 2;

(iii)
For each vector \(\pmb {w}=(\pmb {w}_{\text {in}},\pmb {w}_\text {ex})\in \text {W}^{(0)}\), choose a sample tree \(T_{\pmb {w}}\in {\mathcal {FT}}\) such that \(\pmb {w}_{\text {in}}=\pmb {f}_{{\text {in}}}(T)\) and \(\pmb {w}_\text {ex}=\pmb {f}_{\text {ex}}(T)\), and store these sample trees;

(i)

2.
For each integer \(h=1,2,\ldots , \delta _2\), iteratively execute the next:

(i)
Compute the set \(\text {W}_{\text {end}}^{(h)}\) of all vectors \(\pmb {w}=(\pmb {w}_{\text {in}},\pmb {w}_\text {ex})\) such that \(\pmb {w}_{\text {in}}=\pmb {f}_{{\text {in}}}(T)\) and \(\pmb {w}_\text {ex}=\pmb {f}_{\text {ex}}(T)\) for some birooted tree \(T\in {\mathcal {T}}_{\text {end}}^{(h)}\), where such a vector \(\pmb {w}\) is obtained from a combination of vectors \(\pmb {w}'\in \text {W}^{(0)}\) and \(\pmb {w}''\in \text {W}_{\text {end}}^{(h1)}\);

(ii)
For each vector \(\pmb {w}\in \text {W}_{\text {end}}^{(h)}\), store a sample tree \(T_{\pmb {w}}\), which is obtained from a combination of sample trees \(T_{\pmb {w}'}\) with \(\pmb {w}'\in \text {W}^{(0)}\) and \(T_{\pmb {w}''}\) with \(\pmb {w}''\in \text {W}_{\text {end}}^{(h1)}\);

(i)

3.
We call a pair of vectors \(\pmb {w}^1\in \text {W}_{\text {end}}^{(\delta _1)}\) and \(\pmb {w}^2\in \text {W}_{\text {end}}^{(\delta _2)}\) feasible, if it admits a target graph \(G\in {\mathcal {G}}(\pmb {x}^*)\) such that \(\pmb {w}_{{\text {in}}}^1+\pmb {w}_{{\text {in}}}^2\le \pmb {x}^*_{{\text {in}}}\) and \(\pmb {w}_{\text {ex}}^1+\pmb {w}_{\text {ex}}^2\le \pmb {x}^*_{\text {ex}}\). Find the set \(\text {W}_\text {pair}\) of all feasible pairs of vectors \(\pmb {w}^1\) and \(\pmb {w}^2\);

4.
For each feasible vector pair \((\pmb {w}^1,\pmb {w}^2)\in \text {W}_\text {pair}\), construct a corresponding target graph G by combining the corresponding samples trees \(T_{\pmb {w}^1}\) and \(T_{\pmb {w}^2}\), as illustrated in Fig. 11.
Detailed descriptions of the five steps in the above algorithm can be found in Appendix “Case of two leaf 2branches”.
For a relatively large instance with \(n^*\ge 40\) and \(\text {dia}^*\ge 20\), the number \(\text {W}_\text {pair}\) of feasible vector pairs in Step 4 is still very large. In fact, the size \(\text {W}_{\text {end}}^{(h)}\) of a vector set \(\text {W}_{\text {end}}^{(h)}\) to be computed in Step 2 can also be considerably large during an execution of the algorithm. For such a case, we impose a time limitation on the running time for computing \(\text {W}_{\text {end}}^{(h)}\) and a memory limitation on the number of vectors stored in a vector set \(\text {W}_{\text {end}}^{(h)}\). With these limitations, we can compute only a limited subset \({\widehat{\text {W}}}_{\text {end}}^{(h)}\) of each vector set \(\text {W}_{\text {end}}^{(h)}\) in Step 2. Even with such a subset \({\widehat{\text {W}}}_{\text {end}}^{(h)}\), we still can find a large size of a subset \({\widehat{\text {W}}}_\text {pair}\) of \(\text {W}_\text {pair}\) in Step 3.
Our algorithm also delivers a lower bound on the number of all target graphs \(G\in {\mathcal {G}}(\pmb {x}^*)\) in the following way. In Step 1, we also compute the number \(t(\pmb {w})\) of trees \(T\in {\mathcal {FT}}\) such that \(\pmb {w}=\pmb {f}(T)\) for each \(\pmb {w}\in \text {W}^{(0)}\). In Step 2, when a vector \(\pmb {w}\) is constructed from two vectors \(\pmb {w}'\) and \(\pmb {w}''\), we iteratively compute the number \(t(\pmb {w})\) of trees T such that \(\pmb {w}=\pmb {f}(T)\) by \(t(\pmb {w}):=t(\pmb {w}')\times t(\pmb {w}'')\). In Step 3, when a feasible vector pair \((\pmb {w}^1,\pmb {w}^2)\in \text {W}_\text {pair}\) is obtained, we know that the number of the corresponding target graphs G is \(t(\pmb {w}^1)\times t(\pmb {w}^2)\). Possibly we compute a subset \({\widehat{\text {W}}}_\text {pair}\) of \(\text {W}_\text {pair}\) in Step 3. Then \((1/2)\sum _{(\pmb {w}^1,\pmb {w}^2)\in {\widehat{\text {W}}}_\text {pair}}t(\pmb {w}^1)\times t(\pmb {w}^2)\) gives a lower bound on the number of target graphs \(G\in {\mathcal {G}}(\pmb {x}^*)\), where we divided by 2 since an axially symmetric target graph G can correspond to two vector pairs in \(\text {W}_\text {pair}\).
A sketch of algorithm for computing chemical trirooted trees G with \({\text {bl}}_2(G)=3\)
We call a chemical graph \(G\in {\mathcal {G}}(\pmb {x}^*)\) with diameter \(\text {dia}^*\) and \({\text {bl}}_2(G)=3\) a target graph. Let \(n^*_{\text {inl}}\triangleq \sum _{{\mathtt{a}}\in \Lambda } \pmb {x}^*_{{\text {in}}}({\mathtt{a}})\), which is the number of 2internal vertices in a target graph \(G\in {\mathcal {G}}(\pmb {x}^*)\).
A chemical acyclic graph G with \({\text {bl}}_2(G)=3\) has exactly three leaf 2branches \(v_i\), \(i=1, 2, 3\), and exactly one 2internal vertex \(v_4\) adjacent to three 2internal vertices \(v'_i\), \(i=1, 2, 3\), as illustrated in Fig. 6(b). We call vertex \(v_4\) the jointvertex of G. Without loss of generality assume that the length of the path \(P_{v_1,v_2}\) between \(v_1\) and \(v_2\) is \(\text {dia}^*4\) and that the length of the path \(P_{v_1,v'_1}\) is not smaller than that of \(P_{v_2,v'_2}\).
Analogously with the case of \({\text {bl}}_2(G)=2\), we define internalsubtree (resp., endsubtree, internalfringetree, and endfringetree) of G, to be a connected subgraph \(G'\) that satisfies (76). Observe that G can be partitioned into three endsubtrees \(T_i\), \(i=1, 2, 3\), the 2fringetree \(T_4\) rooted at the jointvertex \(v_4\) and three edges \(v'_i v_4\), \(i=1,2,3\), where the backbone path \(P_{T_i}\) connects leaf 2branch \(v_i\) and vertex \(v'_i\). In particular, we call the endsubtree of G that consists of \(T_1\), \(T_2\), \(T_4\), and edges \(v'_i v_4\), \(i=1,2\), the mainsubtree of G, which consists of the path \(P_{v_1, v_2}\) and all the 2fringetrees rooted at vertices in \(P_{v_1, v_2}\). We call \(T_3\) the cosubtree of G.
Let \(\delta _i\), \(i=1,2,3\) denote the length of the backbone path of \(T_i\). Note that

\(\delta _1+\delta _2+2=\text {dia}^*4\) and \( \delta _1\ge \delta _2\ge \delta _3= n^*_{\text {inl}}  \text {dia}^* + 2\),
from which it follows that

\(\delta _2 \in [\delta _3, \lfloor \text {dia}^*/2 \rfloor 3 ] \) and \(\delta _1 \in [\lceil \text {dia}^*/2\rceil 3, \text {dia}^*  6 \delta _3]\).
We regard a target graph \(G\in {\mathcal {G}}(\pmb {x}^*)\) with \({\text {bl}}_2(G)=3\) and diameter \(\text {dia}^*\) as a combination of the mainsubtree and the cosubtree joined with an edge. We represent the cosubtree as a chemical birooted tree T with \(\ell (P_T)=\delta _3\). We represent the mainsubtree of a target graph G as a trirooted tree T with \(\ell (P_T)=\text {dia}4\) so that terminals \(r_1(T)\), \(r_2(T)\), and \(r_3(T)\), correspond to the two leaf 2branches and the jointvertex of G, respectively.
We start with generating chemical rooted trees and then iteratively extend chemical birooted trees T with \(\ell (P_T)=1, 2, \dots , \text {dia}^*6\delta _3\), before we combine two chemical birooted trees \(T'\) and \(T''\) to obtain a chemical trirooted tree \(T_{1}\) with \(\ell (P_{T_{1}})=\delta _i\), and finally, combine a chemical trirooted tree \(T_{1}\) and a chemical birooted tree \(T_2\) with \(\ell (P_{T_2})=\delta _3\), to obtain a target graph \(G \in {\mathcal {G}}(\pmb {x}^*)\).
Analogously with the case of \({\text {bl}}_2(G)=2\), we define the set \({\mathcal {T}}(\pmb {x}^*)\) of all birooted trees T, the set \({\mathcal {FT}}\) of all rooted trees \(T\in {\mathcal {T}}(\pmb {x}^*)\) that can be a 2fringetree of a target graph G and the set \({\mathcal {T}}_{\text {end}}^{(h)}\), \(h\in [1, \text {dia}^* 6 \delta _3]\), of all birooted trees \(T\in {\mathcal {T}}(\pmb {x}^*)\) that can be an endsubtree of a target graph G such that \(\ell (P_T)=h\).
We generate target graphs \(G\in {\mathcal {G}}(\pmb {x}^*)\) by the following steps:

1.
Analogously with Step 1 for the case of \({\text {bl}}_2(G)=2\), compute the set \({\mathcal {FT}}\) by a branchandbound algorithm as described in "Step 1: Enumeration of 2fringetrees" section, and the set \(\text {W}^{(0)}\) of all vectors \(\pmb {w}=(\pmb {w}_{\text {in}},\pmb {w}_\text {ex})\) such that \(\pmb {w}_{\text {in}}=\pmb {f}_{{\text {in}}}(T)\) and \(\pmb {w}_\text {ex}=\pmb {f}_{\text {ex}}(T)\) for some tree \(T\in {\mathcal {FT}}\). For each vector \(\pmb {w}\in \text {W}^{(0)}\), store a sample tree \(T_{\pmb {w}}\in {\mathcal {FT}}\), and let \(\text {W}_{\text {end}}^{(0)} \subseteq \text {W}^{(0)}\) be the set of feature vectors of possible endtrees with height 2;

2.
For each integer \(h=1,2,\ldots , \text {dia}^* 6 \delta _3\), compute the set \(\text {W}_{\text {end}}^{(h)}\) of all vectors \(\pmb {w}=(\pmb {w}_{\text {in}},\pmb {w}_\text {ex})\) such that \(\pmb {w}_{\text {in}}=\pmb {f}_{{\text {in}}}(T)\) and \(\pmb {w}_\text {ex}=\pmb {f}_{\text {ex}}(T)\) for some birooted tree \(T\in {\mathcal {T}}_{\text {end}}^{(h)}\). For each vector \(\pmb {w}\in \text {W}_{\text {end}}^{(h)}\), store a sample tree \(T_{\pmb {w}}\);

3.
For each integer \(h\in [\lceil \text {dia}^*/2\rceil 2, \text {dia}^*  5 \delta _3] \), compute the set \(\text {W}_{\text {end}+2}^{(h)}\) of all vectors \(\pmb {w}=(\pmb {w}_{\text {in}},\pmb {w}_\text {ex})\) such that \(\pmb {w}_{\text {in}}=\pmb {f}_{{\text {in}}}(T)\) and \(\pmb {w}_\text {ex}=\pmb {f}_{\text {ex}}(T)\) of some birooted tree T with \(\ell (P_T)=h\) that represents an endsubtree rooted at the jointvertex. For each vector \(\pmb {w}\in \text {W}_{\text {end}+2}^{(h)}\), store a sample tree \(T_{\pmb {w}}\);

4.
For each integer \(\delta _1 \in [\lceil \text {dia}^*/2\rceil 3, \text {dia}^*  6 \delta _3]\), compute the set \(\text {W}_{\text {main}}^{(\delta _1+1)}\) of all vectors \(\pmb {w}=(\pmb {w}_{\text {in}},\pmb {w}_\text {ex})\) such that \(\pmb {w}_{\text {in}}=\pmb {f}_{{\text {in}}}(T)\) and \(\pmb {w}_\text {ex}=\pmb {f}_{\text {ex}}(T)\) for some trirooted tree T that represents the mainsubtree such that the length of the path \(P_{r_2(T),r_3(T)}\) between terminals \(r_2(T)\) and \(r_3(T)\) is \(\delta _1+1\). For each vector \(\pmb {w}\in \text {W}_{\text {main}}^{(\delta _1+1)}\), store a sample tree \(T_{\pmb {w}}\);

5.
We call a pair of vectors \(\pmb {w}^1\in \text {W}_{\text {main}}^{(\delta _1+1)}\) and \(\pmb {w}^2\in \text {W}_{\text {end}}^{(\delta _3)}\) feasible if it admits a target graph \(G\in {\mathcal {G}}(\pmb {x}^*)\) such that \(\pmb {w}_{{\text {in}}}^1+\pmb {w}_{{\text {in}}}^2\le \pmb {x}^*_{{\text {in}}}\) and \(\pmb {w}_{\text {ex}}^1+\pmb {w}_{\text {ex}}^2\le \pmb {x}^*_{\text {ex}}\). Find the set \(\text {W}_\text {pair}\) of all feasible pairs of vectors \(\pmb {w}^1\) and \(\pmb {w}^2\);

6.
For each feasible vector pair \((\pmb {w}^1,\pmb {w}^2)\in \text {W}_\text {pair}\), construct a corresponding target graph G by combining the samples trees \(T_{\pmb {w}^1}\) and \(T_{\pmb {w}^2}\), which correspond to the mainsubtree and the cosubtree of a target graph G, respectively, as illustrated in Fig. 12.
Detailed descriptions of the six steps in the above algorithm can be found in Appendix “Case of three leaf 2branches”.
Frequency vectors of fictitious trees
Let T be a chemical birooted or trirooted tree, where we regard a rooted tree T as a birooted tree with \(r_1(T)=r_2(T)\) for a notational convenience. Recall that our algorithm generates a target graph \(G\in {\mathcal {G}}(\pmb {x}^*)\) as a supergraph of T, where one of terminals \(r_1(T)\) and \(r_2(T)\) can be a 2branch of G. We assume that the second terminal \(r_2(T)\) will be a 2branch of G in such a case in our algorithms.
For an integer \(p\in [1,3]\), let \(T[+p]\) denote a fictitious chemical graph obtained from T by regarding the degree of terminal \(r_1(T)\) as \(\deg _T(r_1(T))+p\). Figure 13 (resp., Fig. 14a) illustrates fictitious trees \(T[+p]\) in the case of \(r_1(T)=r_2(T)\) (resp., \(r_1(T)\ne r_2(T)\)). The frequency vectors \(\pmb {f}_{{\text {in}}}(T[+p])\) and \(\pmb {f}_{\text {ex}}(T[+p])\) are obtained as follows: Let \(d=\deg _T(r_1(T))\), \(v_i\), \(i\in [1,d]\), denote the neighbors of \(r_1(T)\), and \(d_i=\deg _T(v_i)\), \(m_i=\beta (r_1(T) v_i)\), and \(\mu _i=(d, d_i, m_i)\), \(\mu '_i=(d+p, d_i, m_i)\), \(i\in [1,d]\).
For \(r_1(T)=r_2(T)\) and \(d'=d+p\),

\(\pmb {f}_{{\text {in}}}(T[+p])= \pmb {f}_{{\text {in}}}(T) +\pmb {1}_{\text {dg}d'}\pmb {1}_{\text {dg}d}\), \(\displaystyle { \pmb {f}_{\text {ex}}(T[+p])= \pmb {f}_{\text {ex}}(T) + \sum _{1\le i\le d}( \pmb {1}_{\mu '_i} \pmb {1}_{\mu _i}) }\).
For \(r_1(T)\ne r_2(T)\) and \(d'=d+p\), where \(v_d\) denotes the vertex in \(P_T\),

\(\pmb {f}_{{\text {in}}}(T[+1]) = \pmb {f}_{{\text {in}}}(T) +\pmb {1}_{\text {dg}d'}\pmb {1}_{\text {dg}d} +\pmb {1}_{\mu '_d}\pmb {1}_{\mu _d}\),

\(\displaystyle { \pmb {f}_{\text {ex}}(T[+1]) = \pmb {f}_{\text {ex}}(T) +\sum _{1\le i\le d1}(\pmb {1}_{\mu '_i}\pmb {1}_{\mu _i}) }\).
Let T be a chemical trirooted tree, where the third terminal \(r_3(T)\) is in the backbone path \(P_T\) between vertices \(r_1(T)\) and \(r_2(T)\). Let \(T\langle +1\rangle \) denote a fictitious chemical graph obtained from T by regarding the degree of terminal \(r_3(T)\) as \(\deg _T(r_3(T))+1\). Figure 14b illustrates a fictitious trirooted tree \(T\langle +1\rangle \). The frequency vectors \(\pmb {f}_{{\text {in}}}(T\langle +1\rangle )\) and \(\pmb {f}_{\text {ex}}(T\langle +1\rangle )\) are obtained as follows: Let \(d=\deg _T(r_3(T))\), \(v_i\), \(i\in [1,d]\), denote the neighbors of \(r_3(T)\), where \(v_{d1}\) and \(v_{d}\) are contained in the path \(P_T\). For each index \(i\in [1,d]\), let \(d_i=\deg _T(v_i)\), \(m_i=\beta (r_3(T) v_i)\), \(\mu _i=(d, d_i, m_i)\), and \(\mu '_i=(d+1, d_i, m_i)\).
Then
Sets of frequency vectors
For an element \({\mathtt{a}}\in \Lambda \) and integers \(d \in [0, d_\text {max}2]\) and \(m\in [d,{\text {val}}({\mathtt{a}})1]\), let \(\text {W}_{\text {inl}}^{(0)}({\mathtt{a}}, d, m)\) (resp., \(\text {W}_{\text {inl}+3}^{(0)}({\mathtt{a}}, d, m)\)) denote the set of frequency vectors \((\pmb {f}_{{\text {in}}}(T[+2]), \pmb {f}_{\text {ex}}(T[+2]))\) (resp., \((\pmb {f}_{{\text {in}}}(T[+3]), \pmb {f}_{\text {ex}}(T[+3]))\)) of a chemical rooted tree T such that

\(r_1(T)=r_2(T)\), the height of T is at most 2,

\(\alpha (r_1(T))={\mathtt{a}}\), \(\deg _T(r_1(T))=d\), and \(\beta (r_1(T))=m\).
Recall that \(\beta (u)=\sum _{uv\in E}\beta (uv)\), defined in “Preliminary” section.
For an element \({\mathtt{a}}\in \Lambda \) and integers \(d \in [1, d_\text {max}1]\), \(m\in [d,{\text {val}}({\mathtt{a}})1]\), and \(h\ge 0\), let \(\text {W}_{\text {end}}^{(h)}({\mathtt{a}}, d, m)\) (resp., \(\text {W}_{\text {end}+2}^{(h)}({\mathtt{a}}, d, m)\)) denote the set of frequency vectors \((\pmb {f}_{{\text {in}}}(T[+1]), \pmb {f}_{\text {ex}}(T[+1]))\) (resp., \((\pmb {f}_{{\text {in}}}(T[+2]), \pmb {f}_{\text {ex}}(T[+2]))\)) of chemical birooted trees T such that

\(\alpha (r_1(T))={\mathtt{a}}\), \(\deg _T(r_1(T))=d\), \(\beta (r_1(T))=m\), \(\ell (P_T)=h\) and

if \(h=0\) then the height of the tree \(T'\) rooted at \(r_2(T)\) is 2.
Case of two leaf 2branches
Step 1: Enumeration of 2fringetrees
The main task of Step 1 is to compute for each tuple \(({\mathtt{a}},d,m)\) of an element \({\mathtt{a}}\in \Lambda \) and integers \(d \in [1,d_\text {max}1]\) (resp., \(d\in [0,d_\text {max}2]\)) and \(m\in [d,{\text {val}}({\mathtt{a}})1]\) (resp., \(m\in [d,{\text {val}}({\mathtt{a}})2]\)), the set \(\text {W}_{\text {end}}^{(0)}({\mathtt{a}}, d, m)\) (resp., \(\text {W}_{\text {inl}}^{(0)}({\mathtt{a}}, d, m)\)) of all frequency vectors \(\pmb {f}(T[+1])\) (resp., \(\pmb {f}(T[+2])\)) of chemical rooted trees T such that \(r_1(T)=r_2(T)\), \(\alpha (r_1(T))={\mathtt{a}}\), \(\deg _T(r_1(T))=d\) and \(\beta (r_1(T))=m\).
Step 1 first computes the set \({\mathcal {FT}}\) of all possible chemical rooted trees \(T\in {\mathcal {T}}(\pmb {x}^*)\) (where \(r_1(T)=r_2(T)\)) that can be a 2fringetree of a target graph \(G\in {\mathcal {G}}(\pmb {x}^*)\). For this, we design a branchandbound procedure where we append a new vertex one by one to construct a rooted tree with only one child. To design a bounding procedure, we derive a property of the structure of chemical rooted trees that can be a 2fringetree of a target graph.
Let \(G_0\) be a chemical rooted tree with a terminal \(r_0=r_1(G_0)=r_2(G_0)\), where \(\pmb {f}_{{\text {in}}}(\alpha (r_0);G_0)=1\) and \(\pmb {f}_{{\text {in}}}({\mathtt{a}};G_0)=0\), \({\mathtt{a}}\in \Lambda \setminus \{\alpha (r_0)\}\) and \(\pmb {f}_{{\text {in}}}(\gamma ;G_0)=0\), \(\gamma \in \Gamma \). For a vector \(\pmb {x}= (\pmb {x}_{\text {in}}, \pmb {x}_\text {ex})\) with \(\pmb {x}_{\text {in}}, \pmb {x}_\text {ex}\in {\mathbb {Z}}_+^{ {\Lambda \cup \Gamma \cup \text {Bc}\cup \text {Dg}}}\), we call \(G_0\) \(\pmb {x}\)extensible if some chemical acyclic graph \(G\in {\mathcal {G}}(\pmb {x})\) contains \(G_0\) as a subgraph of a 2fringetree T rooted at \(r_0\) in G.
We use the next condition as a bounding procedure when we generate chemical rooted trees in Step 1.
Lemma 3
For a branchparameter \(k=2\), let \(\pmb {x}^* = (\pmb {x}_{\text {in}}^*, \pmb {x}_\text {ex}^*)\) be a vector with \(\pmb {x}_{\text {in}}^*, \pmb {x}_\text {ex}^*\in {\mathbb {Z}}_+^{{\Lambda \cup \Gamma \cup \text {Bc}\cup \text {Dg}}}\), and \(G_0\) be a chemical rooted tree rooted at a vertex \(r_0\) such that \(\pmb {f}(G_0)\le \pmb {x}^*\).

(i)
Graph \(G_0\) is \(\pmb {x}^*\)extensible only when the next holds for any subset \(\Lambda '\subseteq \Lambda \):
$$\begin{aligned} \sum _{{\mathtt{a}}\in \Lambda '} (\pmb {x}_\text {ex}^*({\mathtt{a}})  \pmb {f}_\text {ex}({\mathtt{a}}; G_0))&\le \sum _{\begin{array}{c} \gamma =(\mathtt{a,b},m)\in \Gamma : \\ {\mathtt{a}}\in \Lambda ', \mathtt{b}\in \Lambda \setminus \Lambda ' \end{array}} (\pmb {x}_\text {ex}^*(\gamma )  \pmb {f}_\text {ex}(\gamma ; G_0)) \nonumber \\&\quad + 2 \sum _{\begin{array}{c} \gamma =(\mathtt{a,b},m)\in \Gamma : \\ \mathtt{a,b}\in \Lambda ' \end{array}} (\pmb {x}_\text {ex}^*(\gamma )  \pmb {f}_\text {ex}(\gamma ; G_0)). \end{aligned}$$(78) 
(ii)
Let \(G_1\) denote the chemical rooted tree obtained from \(G_0\) by appending a new atom with an element \(\mathtt{b}\in \Lambda \) to an atom with an element \({\mathtt{a}}\in \Lambda \) in \(G_0\) with a multiplicity q; i.e., we join an atom \({\mathtt{a}}\) in \(G_0\) and a new atom \(\mathtt{b}\) with an adjacencyconfiguration \((\mathtt{a,b},q)\). Then \(G_1\) is \(\pmb {x}^*\)extensible only when the next holds:

\(\pmb {x}_\text {ex}^*({\mathtt{a}})  \pmb {f}_\text {ex}({\mathtt{a}}; G_0)\le \pmb {nb}({\mathtt{a}}) 1\)
for

\(\pmb {nb}({\mathtt{a}})= \displaystyle { \sum _{\begin{array}{c} \gamma =(\mathtt{a,b},m)\in \Gamma : \\ \mathtt{b\ne a}\in \Lambda \end{array} } (\pmb {x}_\text {ex}^*(\gamma )  \pmb {f}_\text {ex}(\gamma ; G_0) ) + 2 \sum _{\gamma =(\mathtt{a,a},m)\in \Gamma } (\pmb {x}_\text {ex}^*(\gamma )  \pmb {f}_\text {ex}(\gamma ; G_0) ) }\).

Proof

(i)
Assume that \(G_0\) is a subgraph of a 2fringetree T in some chemical graph \(G\in {\mathcal {G}}(\pmb {x}^*)\) so that T is rooted at \(r_0\). The lefthand side means the number of the remaining 2external vertices with elements in \(\Lambda '\) in the 2fringetrees in G. Each of such atoms has a neighbor in the connected graph G. The righthand side indicates an upper bound on the number of 2external edges joining elements in \(\Lambda '\) in the 2fringetrees in G.

(ii)
Note that \(\pmb {f}_{\text {ex}[\Lambda \cup \Gamma ]}(G_1) = \pmb {f}_{\text {ex}[\Lambda \cup \Gamma ]}(G_0)+\pmb {1}_\mathtt{b}+\pmb {1}_{\gamma }\). For \(\Lambda '=\{{\mathtt{a}}\}\), the lefthand side in Eq. (78) is \(\pmb {x}^*_\text {ex}({\mathtt{a}}) \pmb {f}_\text {ex}({\mathtt{a}}; G_0)\), which remains unchanged if \(\mathtt{a\ne b}\) (resp., is reduced by 1 if \(\mathtt{a= b}\)); and the righthand side in (78) is \(\pmb {nb}({\mathtt{a}})\), which is reduced by 1 if \(\mathtt{a\ne b}\) (resp., is reduced by 2 if \(\mathtt{a= b}\)). That is, the lefthand side minus the righthand side in (78) is always reduced by 1. This gives the required necessary condition for \(G_1\) to be \(\pmb {x}^*\)extensible.
\(\square \)
Figure 15 illustrates all graph structures of rooted trees T with height at most 2 and only one child satisfying the size constraint (1). For each element \({\mathtt{a}}\in \Lambda \), we enumerate chemical trees \(T\in {\mathcal {T}}(\pmb {x}^*)\) rooted at vertex r with \(\alpha (r)={\mathtt{a}}\) that has only one child by a branchandbound algorithm. Let \({\mathcal {T}}_{\mathtt{a}}\) denote the set of resulting rooted trees for each root element \({\mathtt{a}}\in \Lambda \).
We next enumerate chemical trees \(T\in {\mathcal {T}}(\pmb {x}^*)\) rooted at vertex r with \(\alpha (r)={\mathtt{a}}\) that has two or three children by generating a combination of two or three graphs in \({\mathcal {T}}_{\mathtt{a}}\). During generating graphs, our bounding procedure tests whether the current graph satisfies the necessary condition in Lemma 3(ii).
Finally, we compute the following sets:
for each element \({\mathtt{a}}\in \Lambda \), integers \(d\in [1,d_\text {max}1]\), \(m\in [d,{\text {val}}({\mathtt{a}})1]\), the set \(\text {W}_{\text {end}}^{(0)}({\mathtt{a}}, d, m)\) of frequency vectors \(\pmb {f}(T[+1])\) for rooted trees \(T\in {\mathcal {T}}_{\mathtt{a}}\) with \(\deg _T(r)=d\) and height 2;
for each element \({\mathtt{a}}\in \Lambda \), integers \(d\in [0,d_\text {max}2]\), \(m\in [d,{\text {val}}({\mathtt{a}})2]\), the set \(\text {W}_{\text {inl}}^{(0)}({\mathtt{a}}, d, m)\) of frequency vectors \(\pmb {f}(T[+2])\) for rooted trees \(T\in {\mathcal {T}}_{\mathtt{a}}\) with \(\deg _T(r)=d\) and height at most 2.
For each vector \(\pmb {w}\in \text {W}_{\text {end}}^{(0)}({\mathtt{a}}, d, m)\) (resp., \(\pmb {w}\in \text {W}_{\text {inl}}^{(0)}({\mathtt{a}}, d, m)\)), we store a sample tree \(T_{\pmb {w}}\).
We remark that the size of the set \({\mathcal {FT}}\) depends on the vector \(\pmb {x}^*\). However, since the height of trees is limited to 2, the degree is at most 3 or 4, and the size constraint (1) on fringe trees in "Our target graph class" section, the size of the set \({\mathcal {FT}}\) is fairly limited.
Step 2: Generation of frequency vectors of endsubtrees
The main task of Step 2 is to compute the following sets in the ascending order of \(h=1,2,\ldots , \delta _2 \):
For elements \({\mathtt{a}}\in \Lambda \), integers \(d \in [1,d_\text {max}1]\), \(m \in [d ,{\text {val}}({\mathtt{a}})1]\), and \(h\in [ 1, \delta _2 ]\), the sets \(\text {W}_{\text {end}}^{(h)}({\mathtt{a}}, d , m )\) of all frequency vectors \(\pmb {f}(T[+1])\) of chemical birooted trees \(T\in {\mathcal {T}}(\pmb {x}^*)\) such that \(\alpha (r_1(T))={\mathtt{a}}\), \(\deg _T(r_1(T))=d \), \(\beta (r_1(T))=m\) and \(\ell (P_T)=h\).
Observe that each vector \(\pmb {w}=(\pmb {w}_{{\text {in}}},\pmb {w}_{\text {ex}})\in \text {W}_{\text {end}}^{(h)}({\mathtt{a}}, d , m )\) is obtained from a combination of vectors \(\pmb {w}'=(\pmb {w}'_{{\text {in}}},\pmb {w}'_{\text {ex}})\in \text {W}_{\text {inl}}^{(0)}({\mathtt{a}}, d 1, m')\) and \(\pmb {w}''=(\pmb {w}''_{{\text {in}}},\pmb {w}''_{\text {ex}}) \in \text {W}_{\text {end}}^{(h1)}(\mathtt{b}, d'', m'')\) such that

\(m' \le {\text {val}}({\mathtt{a}})  2\), \(1\le m  m' \le {\text {val}}(b) m'' \),

\(\pmb {w}_{{\text {in}}} = \pmb {w}'_{{\text {in}}} + \pmb {w}''_{{\text {in}}} + \pmb {1}_\gamma + \pmb {1}_\mu \le \pmb {x}_{{\text {in}}}^*\), \(\pmb {w}_{\text {ex}} = \pmb {w}'_{\text {ex}} + \pmb {w}''_{\text {ex}} \le \pmb {x}_{\text {ex}}^*\)

for \(\gamma = ({\mathtt{a}}, \mathtt{b}, m  m' ) \in \Gamma \) and \(\mu = (d+1, d''+1, m  m') \in \text {Bc}\).
Figure 16 illustrates this process of computing a vector \(\pmb {w}\in \text {W}_{\text {end}}^{(h)}({\mathtt{a}}, d , m )\).
For each vector \(\pmb {w}\in \text {W}_{\text {end}}^{(h)}({\mathtt{a}}, d , m )\) obtained from a combination \(\pmb {w}'\in \text {W}_{\text {inl}}^{(0)}({\mathtt{a}}, d 1, m')\) and \(\pmb {w}'' \in \text {W}_{\text {end}}^{(h1)}(\mathtt{b}, d'', m'')\), we construct a sample tree \(T_{\pmb {w}}\) from their sample trees \(T_{\pmb {w}'}\) and \(T_{\pmb {w}''}\).
Step 3: Enumeration of feasible vector pairs
A feasible pair of vectors is defined to be a pair of vectors \(\pmb {w}^i= (\pmb {w}^i_{{\text {in}}}, \pmb {w}^i_{\text {ex}}) \in \text {W}_{\text {end}}^{(\delta _i)} ({\mathtt{a}}_i,d_i,m_i)\), \({\mathtt{a}}_i\in \Lambda \), \(d_i\in [1,d_\text {max}1]\), \(m_i\in [d_i,{\text {val}}({\mathtt{a}}_i)1]\), \(i = 1,2\) that admits an adjacencyconfiguration \(\gamma = ({\mathtt{a}}_1, {\mathtt{a}}_2, m) \in \Gamma \) and a bondconfiguration \(\mu = (d_1 + 1, d_2 + 1, m) \in \text {Bc}\) with an integer \(m \in [1, \min \{3, {\text {val}}({\mathtt{a}}_1)  m_1, {\text {val}}({\mathtt{a}}_2)  m_2\} ]\) such that

\(\pmb {x}^*_{{\text {in}}} = \pmb {w}^1_{{\text {in}}} + \pmb {w}^2_{{\text {in}}} + \pmb {1}_{\gamma } + \pmb {1}_{\mu }\) and \(\pmb {x}^*_{\text {ex}} = \pmb {w}^1_{\text {ex}} + \pmb {w}^2_{\text {ex}}\),
or equivalently \(\pmb {w}^1\) is equal to the vector \((\pmb {x}^*_{{\text {in}}}  \pmb {w}^2_{{\text {in}}}\pmb {1}_{\gamma } \pmb {1}_{\mu }, \pmb {x}^*_{\text {ex}}  \pmb {w}^1_{\text {ex}})\), which we call the \((\gamma , \mu )\)complement of \(\pmb {w}^2\), and denote it by \(\overline{\pmb {w}^2}\).
The main task of Step 3 is to enumerate all feasible vector pairs \((\pmb {w}^1,\pmb {w}^2)\), \(\pmb {w}^i \in \text {W}_{\text {end}}^{(\delta _i)}({\mathtt{a}}_i,d_i,m_i)\) with \({\mathtt{a}}_i\in \Lambda \), \(d_i\in [1,d_\text {max}1]\), \(m_i\in [d_i,{\text {val}}({\mathtt{a}}_i)1]\), \(i = 1,2\).
To efficiently search for a feasible pair of vectors in two sets \(\text {W}_{\text {end}}^{(\delta _i)}({\mathtt{a}}_i,d_i,m_i)\), \(i = 1,2\), we first compute the \((\gamma , \mu )\)complement vector \(\overline{\pmb {w}}\) of each vector \(\pmb {w}\in \text {W}_{\text {end}}^{(\delta _2)}({\mathtt{a}}_2,d_2,m_2)\) for each pair of \( \gamma = ({\mathtt{a}}_1, {\mathtt{a}}_2, m) \in \Gamma \) and \(\mu = (d_1 + 1, d_2 + 1, m) \in \text {Bc}\) with \(m \in [1, \min \{3, {\text {val}}({\mathtt{a}}_1)  m_1, {\text {val}}({\mathtt{a}}_2)  m_2\} ]\), and denote by \(\overline{\text {W}_{\text {end}}^{(\delta _2)}}\) the set of the resulting \((\gamma , \mu )\)complement vectors. Observe that \((\pmb {w}^1,\pmb {w}^2)\) is a feasible vector pair if and only if \(\pmb {w}_1=\overline{\pmb {w}_2}\). To find such pairs, we merge the sets \(\text {W}_{\text {end}}^{(\delta _1)}({\mathtt{a}}_1,d_1,m_1)\) and \(\overline{\text {W}_{\text {end}}^{(\delta _2)}}\) into a sorted list \(L_{\gamma , \mu }\). Then each feasible vector pair \((\pmb {w}^1,\pmb {w}^2)\) appears as a consecutive pair of vectors \(\pmb {w}_1\) and \(\overline{\pmb {w}_2}\) in the list \(L_{\gamma , \mu }\).
Step 4: Construction of chemical graphs
The task of Step 4 is to construct for each feasible vector pair \(\pmb {w}^i\in \text {W}_{\text {end}}^{(\delta _i)}({\mathtt{a}}_i,d_i,m_i)\), \(i = 1,2\) such that \(\pmb {w}^1\) is equal to the \((\gamma = ({\mathtt{a}}_1, {\mathtt{a}}_2, m), \mu )\)complement vector \(\overline{\pmb {w}^2}\) of \(\pmb {w}^2\), construct a target graph \(T_{(\pmb {w}_1, \pmb {w}_2)}\in {\mathcal {G}}(\pmb {x}^*)\) by combining the sample trees \(T_i=T_{\pmb {w}^i}\) of vectors \(\pmb {w}^i\) with an edge \(e=r_1(T_1)r_1(T_2)\) such that \(\beta (e)=m\). Figure 11 illustrates two sample trees \(T_i\), \(i=1,2\) to be combined with a new edge \(e=r_1(T_1)r_1(T_2)\).
Case of three leaf 2branches
Step 1: Enumeration of 2fringetrees
The main task of Step 1 is to compute the following sets:
for each tuple \(({\mathtt{a}},d,m)\) of an element \({\mathtt{a}}\in \Lambda \) and integers \(d \in [1,d_\text {max}1]\) (resp., \(d\in [0,d_\text {max}2]\) and \(d\in [0,d_\text {max}3]\)) and \(m\in [d,{\text {val}}({\mathtt{a}})1]\) (resp., \(m\in [d,{\text {val}}({\mathtt{a}})2]\) and \(m\in [d,{\text {val}}({\mathtt{a}})3]\)), the set \(\text {W}_{\text {end}}^{(0)}({\mathtt{a}}, d, m)\) (resp., \(\text {W}_{\text {inl}}^{(0)}({\mathtt{a}}, d, m)\) and \(\text {W}_{\text {inl}+3}^{(0)}({\mathtt{a}}, d, m)\)) of all frequency vectors \(\pmb {f}(T[+1])\) (resp., \(\pmb {f}(T[+2])\) and \(\pmb {f}(T[+3])\)) of chemical rooted trees T such that \(r_1(T)=r_2(T)\), \(\alpha (r_1(T))={\mathtt{a}}\), \(\deg _T(r_1(T))=d\) and \(\beta (r_1(T))=m\). For each vector \(\pmb {w}\in \text {W}_{\text {end}}^{(0)}({\mathtt{a}}, d, m)\) (resp., \(\pmb {w}\in \text {W}_{\text {inl}}^{(0)}({\mathtt{a}}, d, m)\) and \(\pmb {w}\in \text {W}_{\text {inl}+3}^{(0)}({\mathtt{a}}, d, m)\)), we store a sample tree \(T_{\pmb {w}}\). This step can be designed in a similar way as Step 1 for the case of \({\text {bl}}_2(G)=2\).
Step 2: Generation of frequency vectors of endsubtrees
Analogously with Step 2 for the case of \({\text {bl}}_2(G)=2\), Step 2 computes the following sets in the ascending order of \(h=1,2,\ldots , \text {dia}^* 6 \delta _3\):
For elements \({\mathtt{a}}\in \Lambda \), integers \(d \in [1,d_\text {max}1]\), \(m \in [d ,{\text {val}}({\mathtt{a}})1]\), \(i=1,2\), and \(h\in [ 1, \text {dia}^* 6 \delta _3]\), the sets \(\text {W}_{\text {end}}^{(h)}({\mathtt{a}},d,m)\) of all frequency vectors \(\pmb {f}(T[+1])\) of chemical birooted trees \(T\in {\mathcal {T}}(\pmb {x}^*)\) such that \(\alpha (r_1(T))={\mathtt{a}}\), \(\deg _T(r_1(T))=d\), \(\beta (r_1(T))=m\) and \(\ell (P_T)=h\).
For each vector \(\pmb {w}\in \text {W}_{\text {end}}^{(h)}({\mathtt{a}}, d, m)\), we construct a sample tree \(T_{\pmb {w}}\) from their sample trees \(T_{\pmb {w}'}\) and \(T_{\pmb {w}''}\).
Step 3: Generation of frequency vectors of endsubtrees with two fictitious edges
The main task of Step 3 is to compute the following sets:
For elements \({\mathtt{a}}\in \Lambda \), integers \(d \in [1,d_\text {max}2]\), \(m\in [d,{\text {val}}({\mathtt{a}})2]\) and \(h\in [\lceil \text {dia}^*/2\rceil 2, \text {dia}^*  5 \delta _3] \), the sets \(\text {W}_{\text {end}+2}^{(h)}({\mathtt{a}},d,m)\) of all frequency vectors of birooted trees \(T[+2]\) such that \(\alpha (r_1(T))={\mathtt{a}}\), \(\deg _T(r_1(T))=d\), \(\beta (r_1(T))=m\) and \(\ell (P_T)=h\). For each vector \(\pmb {w}\in \text {W}_{\text {end}+2}^{(h)}({\mathtt{a}},d,m)\), we store a sample tree \(T_{\pmb {w}}\). This step can be designed in a similar way as Step 3 for the case of \({\text {bl}}_2(G)=2\).
Step 4: Enumeration of frequency vectors of mainsubtrees
For an element \({\mathtt{a}}\in \Lambda \), and integers \(d \in [2,d_\text {max}1]\), \(m \in [d, {\text {val}}({\mathtt{a}}) 1]\), and \(\delta _1 \in [\lceil \text {dia}^*/2\rceil 3, \text {dia}^*  6 \delta _3]\), define \(\text {W}_{\text {main}}^{(\delta _1+1)}({\mathtt{a}}, d, m)\) to be the set of the frequency vectors \(\pmb {f}(T\langle +1\rangle )\) of chemical trirooted trees T such that

\(\alpha (r_1(T))={\mathtt{a}}\), \(\deg _T(r_1(T))=d\), \(\beta (r_1(T))=m\), \(\ell (P_T)=\text {dia}^*4\) and

the length of the path \(P_{r_2(T),r_3(T)}\) between vertices \(r_2(T)\) and \(r_3(T)\) is \(\delta _1+1\).
See Fig. 12 for the structure of a maintree. Such a chemical trirooted graph T corresponds to the mainsubtree of a target graph \(G\in {\mathcal {G}}(\pmb {x}^*)\).
The main task of Step 4 is to compute the sets \(\text {W}_{\text {main}}^{(\delta _1+1)}({\mathtt{a}}, d, m)\), \({\mathtt{a}}\in \Lambda \), \(d \in [2,d_\text {max}1]\), \(m \in [d, {\text {val}}({\mathtt{a}}) 1]\), \(\delta _1 \in [\lceil \text {dia}^*/2\rceil 3, \text {dia}^*  6 \delta _3]\). Each vector \(\pmb {w}\in \text {W}_{\text {main}}^{(\delta _1+1)}({\mathtt{a}}, d, m)\) can be obtained from a combination of vectors \(\pmb {w}^1\in \text {W}_{\text {end}+2}^{(\delta _1+1)}({\mathtt{a}}, d1, m'')\) and \(\pmb {w}^2\in \text {W}_{\text {end}}^{(\delta _2)}({\mathtt{a}}', d', m')\) such that \(\delta _1 + \delta _2 = \text {dia}^*  4\) and \(\delta _1 \ge \delta _2 \), as illustrated in Fig. 17. For each vector \(\pmb {w}\in \text {W}_{\text {main}}^{(\delta _1+1)}({\mathtt{a}}, d, m)\), we store a sample tree \(T_{\pmb {w}}\). This step can be designed in a similar way as Step 3 for the case of \({\text {bl}}_2(G)=2\).
Step 5: Enumeration of feasible vector pairs
Analogously with the case of \({\text {bl}}_2(G)=2\), a feasible pair of vectors is defined to be a pair of vectors \(\pmb {w}^1= (\pmb {w}^1_{{\text {in}}}, \pmb {w}^1_{\text {ex}}) \in \text {W}_{\text {main}}^{(\delta _1+1)}({\mathtt{a}}_1,d_1, m_1)\), and \(\pmb {w}^2= (\pmb {w}^2_{{\text {in}}}, \pmb {w}^2_{\text {ex}}) \in \text {W}_{\text {end}}^{(\delta _3)}({\mathtt{a}}_2,d_2, m_2)\), \(\delta _1 \in [\lceil \text {dia}^*/2\rceil 3, \text {dia}^*  6 \delta _3]\), \({\mathtt{a}}_i\in \Lambda \), \(d_i\in [1,d_\text {max}1]\), \(m_i\in [d_i,{\text {val}}({\mathtt{a}}_i)1]\), \(i = 1,2\) that admits an adjacencyconfiguration \(\gamma = ({\mathtt{a}}_1, {\mathtt{a}}_2, m) \in \Gamma \) and a bondconfiguration \(\mu = (d_1 + 1, d_2 + 1, m) \in \text {Bc}\) with an integer \(m \in [1, \min \{3, {\text {val}}({\mathtt{a}}_1)  m_1, {\text {val}}({\mathtt{a}}_2)  m_2\} ]\) such that

\(\pmb {x}^*_{{\text {in}}} = \pmb {w}^1_{{\text {in}}} + \pmb {w}^2_{{\text {in}}} + \pmb {1}_{\gamma } + \pmb {1}_{\mu }\) and \(\pmb {x}^*_{\text {ex}} = \pmb {w}^1_{\text {ex}} + \pmb {w}^2_{\text {ex}}\).
Step 5 computes the set of all feasible vector pairs \((\pmb {w}^1,\pmb {w}^2)\) by using a sorting algorithm as in the Step 4 for the case of \({\text {bl}}_2(G)=2\).
Step 6: Construction of chemical graphs
Analogously with Step 4 for the case of \({\text {bl}}_2(G)=2\), Step 6 constructs a target graph \(T_{(\pmb {w}_1, \pmb {w}_2)}\in {\mathcal {G}}(\pmb {x}^*)\) for each feasible vector pair \((\pmb {w}^1,\pmb {w}^2)\) by combining the sample trees \(T_i=T_{\pmb {w}^i}\) of vectors \(\pmb {w}^i\) with a new edge \(e=r_1(T_1)r_1(T_2)\).
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
About this article
Cite this article
Azam, N.A., Zhu, J., Sun, Y. et al. A novel method for inference of acyclic chemical compounds with bounded branchheight based on artificial neural networks and integer programming. Algorithms Mol Biol 16, 18 (2021). https://doi.org/10.1186/s13015021001972
Received:
Accepted:
Published:
Keywords
 QSAR/QSPR
 Molecular design
 Artificial neural network
 Mixed integer linear programming
 Enumeration of graphs
AMS Subject Classification
 Primary
 05C92
 92E10
 Secondary
 05C30
 68T07
 90C11
 9204