Multi-Objective Drug Design Based on Graph-Fragment Molecular Representation and Deep Evolutionary Learning

Muhetaer Mukaidaisi; Andrew Vu; Karl Grantham; Alain Tchagang; Yifeng Li

doi:10.3389/fphar.2022.920747

Multi-Objective Drug Design Based on Graph-Fragment Molecular Representation and Deep Evolutionary Learning

Front Pharmacol. 2022 Jul 4:13:920747. doi: 10.3389/fphar.2022.920747. eCollection 2022.

Authors

Muhetaer Mukaidaisi¹, Andrew Vu¹, Karl Grantham¹, Alain Tchagang², Yifeng Li¹

Affiliations

¹ Biomedical Data Science Laboratory, Department of Computer Science, Brock University, St. Catharines, ON, Canada.
² Scientific Data Mining Team, Digital Technologies Research Centre, National Research Council Canada, Ottawa, ON, Canada.

Abstract

Drug discovery is a challenging process with a huge molecular space to be explored and numerous pharmacological properties to be appropriately considered. Among various drug design protocols, fragment-based drug design is an effective way of constraining the search space and better utilizing biologically active compounds. Motivated by fragment-based drug search for a given protein target and the emergence of artificial intelligence (AI) approaches in this field, this work advances the field of in silico drug design by (1) integrating a graph fragmentation-based deep generative model with a deep evolutionary learning process for large-scale multi-objective molecular optimization, and (2) applying protein-ligand binding affinity scores together with other desired physicochemical properties as objectives. Our experiments show that the proposed method can generate novel molecules with improved property values and binding affinities.

Keywords: deep evolutionary learning; drug design; graph fragmentation; multi-objective optimization; protein-ligand binding affinity; variational autoencoder.