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

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.

arXiv commentarXiv admin note: substantial text overlap with arXiv:2107.02381

References in corpus (6)

• Semi-Supervised Classification with Graph Convolutional Networks
• MolGAN: An implicit generative model for small molecular graphs
• ChemTS: An Efficient Python Library for de novo Molecular Generation
• A novel method for inference of chemical compounds with prescribed topological substructures based on integer programming
• A Novel Method for Inference of Acyclic Chemical Compounds with Bounded Branch-height Based on Artificial Neural Networks and Integer Programming
• A Method for Inferring Polymers Based on Linear Regression and Integer Programming