Deep Generative Models for Molecule Optimization
Speaker: Xia Ning, Ohio State University
Abstract: Molecule optimization is a critical step in drug development to improve desired properties of drug candidates through chemical modification. In this talk, I will present a novel deep generative model Modof over molecular graphs for molecule optimization. We developed Modof leveraging the most advanced deep learning approaches that enable profound molecule structure representation learning and new molecule generation through sampling from molecule representations and encoding. Following the rationale of fragment-based drug design, Modof modifies a given molecule by predicting a single site of disconnection at the molecule and the removal and/or addition of fragments at that site. A pipeline of multiple, identical Modof models is implemented into Modof-pipe to optimize molecules at multiple disconnection sites. Here we show that Modof-pipe can retain major molecular scaffolds, allow controls over intermediate optimization steps, and better constrain molecule similarities. Modof-pipe outperforms the state-of-the-art methods on benchmark datasets, with a 121.0% property improvement without molecular similarity constraints, and 82.0% and 10.6% improvement if the optimized molecules are at least 0.2 and 0.4 similar to those before optimization, respectively.