Optimized Drug Design using Multi-Objective Evolutionary Algorithms with SELFIES

TL;DR

This paper demonstrates the use of multi-objective evolutionary algorithms combined with SELFIES representation to efficiently explore chemical space and identify promising drug candidates with optimized properties.

Contribution

It introduces the application of NSGA-II, NSGA-III, and MOEA/D algorithms with SELFIES for multi-objective drug design, advancing computational methods in medicinal chemistry.

Findings

All algorithms successfully optimized drug-like properties.

Algorithms converged and identified diverse solutions.

Promising candidates for synthesis were discovered.

Abstract

Computer aided drug design is a promising approach to reduce the tremendous costs, i.e. time and resources, for developing new medicinal drugs. It finds application in aiding the traversal of the vast chemical space of potentially useful compounds. In this paper, we deploy multi-objective evolutionary algorithms, namely NSGA-II, NSGA-III, and MOEA/D, for this purpose. At the same time, we used the SELFIES string representation method. In addition to the QED and SA score, we optimize compounds using the GuacaMol benchmark multi-objective task sets. Our results indicate that all three algorithms show converging behavior and successfully optimize the defined criteria whilst differing mainly in the number of potential solutions found. We observe that novel and promising candidates for synthesis are discovered among obtained compounds in the Pareto-sets.