A Deep Generative Model for Molecule Optimization via One Fragment Modification
This work addresses the critical problem of improving drug candidate properties through chemical modification, which is significant for drug development. It offers an incremental improvement over existing state-of-the-art methods.
The authors developed Modof-pipe, a deep generative model for molecule optimization that modifies molecules by predicting single disconnection sites and adding/removing fragments. Modof-pipe achieved an 81.2% improvement in octanol-water partition coefficient (penalized by synthetic accessibility and ring size) without molecular similarity constraints, and 51.2%, 25.6%, and 9.2% improvement with similarity constraints of 0.2, 0.4, and 0.6 respectively. An enhanced version, Modof-pipem, further improved performance by at least 17.8%.
Molecule optimization is a critical step in drug development to improve desired properties of drug candidates through chemical modification. We developed a novel deep generative model Modof over molecular graphs for molecule optimization. Modof modifies a given molecule through the prediction of 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 modify an input molecule at multiple disconnection sites. Here we show that Modof-pipe is able to 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: without molecular similarity constraints, Modof-pipe achieves 81.2% improvement in octanol-water partition coefficient penalized by synthetic accessibility and ring size; and 51.2%, 25.6% and 9.2% improvement if the optimized molecules are at least 0.2, 0.4 and 0.6 similar to those before optimization, respectively. Modof-pipe is further enhanced into Modof-pipem to allow modifying one molecule to multiple optimized ones. Modof-pipem achieves additional performance improvement as at least 17.8% better than Modof-pipe.