Multi-Objective Drug Design Based on Graph-Fragment Molecular Representation and Deep Evolutionary Learning - Mukaidaisi, Muhetaer; Vu, Andrew; Grantham, Karl; Tchagang, Alain; Li, Yifeng (Frontiers in Pharmacology, 2022)
Drug discovery is a challenging process with a huge molecular space to be explored and numerous pharmacological properties to be appropriately considered. Among a range of drug design protocols, fragment-based drug design is an effective way of constraining the search space and better utilizing biologically active compounds. This work advances the field of in silico drug design by integrating a graph fragmentation-based deep generative model with the deep evolutionary learning process for large-scale multi-objective molecular optimization. Through applying protein-ligand binding affinity scores together with other desired physicochemical properties as objectives, our method is able to generate novel molecules with improved property values and binding affinities.