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

Muhetaer Mukaidaisi, Andrew Vu, Karl Grantham, Alain Tchagang, Yifeng Li
2022 Frontiers in Pharmacology  
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
more » ... n 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.
doi:10.3389/fphar.2022.920747 pmid:35860028 pmcid:PMC9291509 fatcat:g6xq4hcw3jetvb43x2dtugvd6i