A Novel Method for Inference of Chemical Compounds of Cycle Index Two with Desired Properties Based on Artificial Neural Networks and Integer Programming

Tatsuya Akutsu,Aleksandar Shurbevski,Jianshen Zhu,Chenxi Wang,Hiroshi Nagamochi

doi:10.3390/a13050124

Abstract

Inference of chemical compounds with desired properties is important for drug design, chemo-informatics, and bioinformatics, to which various algorithmic and machine learning techniques have been applied. Recently, a novel method has been proposed for this inference problem using both artificial neural networks (ANN) and mixed integer linear programming (MILP). This method consists of the training phase and the inverse prediction phase. In the training phase, an ANN is trained so that the output of the ANN takes a value nearly equal to a given chemical property for each sample. In the inverse prediction phase, a chemical structure is inferred using MILP and enumeration so that the structure can have a desired output value for the trained ANN. However, the framework has been applied only to the case of acyclic and monocyclic chemical compounds so far. In this paper, we significantly extend the framework and present a new method for the inference problem for rank-2 chemical compounds (chemical graphs with cycle index 2). The results of computational experiments using such chemical properties as octanol/water partition coefficient, melting point, and boiling point suggest that the proposed method is much more useful than the previous method.

Highlights

Inference of chemical compounds with desired properties is important for computer-aided drug design
We implemented our method of Steps 1 to 5 for inferring rank-2 chemical graphs and conducted experiments to evaluate the computational efficiency for three chemical properties π: octanol/water partition coefficient (K OW), melting point (M P), and boiling point (B P)
For each property π ∈ { K OW, M P, B P }, we select a set Λ of chemical elements and collected a data set Dπ on rank-2 chemical graphs over Λ provided by HSDB from PubChem

Summary

Introduction

Inference of chemical compounds with desired properties is important for computer-aided drug design. Since drug design is one of the major targets of chemo-informatics and bioinformatics, it is important in these areas. This problem has been extensively studied in chemo-informatics under the name of inverse QSAR/QSPR [1,2], where QSAR/QSPR denotes Quantitative Structure. Since chemical compounds are usually represented as undirected graphs, this problem is important from graph theoretic and algorithmic viewpoints. Inverse QSAR/QSPR is often formulated as an optimization problem to find a chemical graph maximizing (or minimizing) an objective function under various constraints, where objective functions reflect certain chemical activities or properties. Objective functions are derived from a set of training data consisting of known molecules and their activities/properties using statistical and machine learning methods

Methods

Results

Conclusion