OptiMol: Optimization of binding affinities in chemical space for drug discovery

Abstract

Ligand-based drug design has recently benefited from the boost of deep generative models. These models enable extensive explorations of the chemical space, and provide a platform for molecular optimization. However, current state of the art methods do not leverage the structure of the target, which is known to play a key role in the interaction. We propose an optimization pipeline that leverages complementary structure-based and ligand-based methods. Instead of performing docking on a fixed drug bank, we iteratively select promising compounds in the whole chemical space using a ligand-centered generative model. Molecular docking is then used as an oracle to guide compound optimization. This allows to iteratively generate leads that better fit the target structure, in a closed optimization loop, without prior knowledge about bio-actives. For this purpose, we introduce a new graph to selfies VAE which benefits from a seventeen times faster decoding than graph to graph methods while being competitive with the state of the art. We then successfully optimize the generation of molecules towards high docking scores, enabling a ten-fold augmentation of high-scoring compounds found with a fixed computational budget. AvailabilityCode is available on GitHub Contactjeromew@mcgill.ca