Molecule Optimization via Fragment-based Generative Models

In drug discovery, molecule optimization is an important step in order to modify drug candidates into better ones in terms of desired drug properties. With the recent advance of Artificial Intelligence, this traditionally in vitro process has been increasingly facilitated by in silico approaches. We present an innovative in silico approach to computationally optimizing molecules and formulate the problem as to generate optimized molecular graphs via deep generative models. Our generative models follow the key idea of fragment-based drug design, and optimize molecules by modifying their small fragments. Our models learn how to identify the to-be-optimized fragments and how to modify such fragments by learning from the difference of molecules that have good and bad properties. In optimizing a new molecule, our models apply the learned signals to decode optimized fragments at the predicted location of the fragments. We also construct multiple such models into a pipeline such that each of the models in the pipeline is able to optimize one fragment, and thus the entire pipeline is able to modify multiple fragments of molecule if needed. We compare our models with other state-of-the-art methods on benchmark datasets and demonstrate that our methods significantly outperform others with more than 80% property improvement under moderate molecular similarity constraints, and more than 10% property improvement under high molecular similarity constraints.