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

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.

Automated summary

Fragment-based drug design is an effective method to constrain the search space and better utilize biologically active compounds. This study integrates a graph fragmentation-based deep generative model with deep evolutionary learning for large-scale multi-objective molecular optimization, and applies protein-ligand binding affinity scores and other desired physicochemical properties as objectives. Experimental results show that the proposed method can generate novel molecules with improved property values and binding affinities.