Genetic Algorithm for Constrained Molecular Inverse Design

TL;DR

This paper presents a genetic algorithm for constrained molecular inverse design that efficiently explores large chemical spaces, optimizing specific properties while maintaining structural constraints through a two-phase process.

Contribution

It introduces a novel genetic algorithm that effectively handles structural constraints and property optimization in molecular design.

Findings

Successfully generates valid molecules for crossover and mutation

Optimizes pharmacological properties while maintaining molecular substructure

Proves effectiveness through experimental validation

Abstract

A genetic algorithm is suitable for exploring large search spaces as it finds an approximate solution. Because of this advantage, genetic algorithm is effective in exploring vast and unknown space such as molecular search space. Though the algorithm is suitable for searching vast chemical space, it is difficult to optimize pharmacological properties while maintaining molecular substructure. To solve this issue, we introduce a genetic algorithm featuring a constrained molecular inverse design. The proposed algorithm successfully produces valid molecules for crossover and mutation. Furthermore, it optimizes specific properties while adhering to structural constraints using a two-phase optimization. Experiments prove that our algorithm effectively finds molecules that satisfy specific properties while maintaining structural constraints.