Molecular Design Based on Artificial Neural Networks, Integer Programming and Grid Neighbor Search

TL;DR

This paper introduces a new method for molecular design that combines neural networks, integer programming, and neighbor search to generate diverse chemical structures with desired properties.

Contribution

It proposes a neighbor search procedure integrated into an existing neural network and integer programming framework for generating multiple feasible molecular structures.

Findings

Can generate up to 50 non-hydrogen atom chemical graphs

Enhances diversity of molecular structures in design process

Effective in producing additional feasible solutions

Abstract

A novel framework has recently been proposed for designing the molecular structure of chemical compounds with a desired chemical property using both artificial neural networks and mixed integer linear programming. In the framework, a chemical graph with a target chemical value is inferred as a feasible solution of a mixed integer linear program that represents a prediction function and other requirements on the structure of graphs. In this paper, we propose a procedure for generating other feasible solutions of the mixed integer linear program by searching the neighbor of output chemical graph in a search space. The procedure is combined in the framework as a new building block. The results of our computational experiments suggest that the proposed method can generate an additional number of new chemical graphs with up to 50 non-hydrogen atoms.