AUTHOR=Mukaidaisi Muhetaer , Vu Andrew , Grantham Karl , Tchagang Alain , Li Yifeng TITLE=Multi-Objective Drug Design Based on Graph-Fragment Molecular Representation and Deep Evolutionary Learning JOURNAL=Frontiers in Pharmacology VOLUME=13 YEAR=2022 URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2022.920747 DOI=10.3389/fphar.2022.920747 ISSN=1663-9812 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.