De Novo Drug Design Using Reinforcement Learning with Graph- Based Deep Generative Models

Machine learning provides effective computational tools for exploring the chemical space via deep generative models. Here, we propose a new reinforcement learning scheme to finetune graph-based deep generative models for de novo molecular design tasks. We show how our computational framework can successfully guide a pretrained generative model toward the generation of molecules with a specific property profile, even when such molecules are not present in the training set and unlikely to be generated by the pretrained model. We explored the following tasks: generating molecules of decreasing/increasing size, increasing drug-likeness, and increasing bioactivity. Using the proposed approach, we achieve a model which generates diverse compounds with predicted DRD2 activity for 95% of sampled molecules, outperforming previously reported methods on this metric.

Automated summary

This research proposes a new reinforcement learning scheme to fine-tune graph-based deep generative models for de novo molecular design. The proposed approach can successfully guide a pretrained generative model to generate molecules with specific properties, even if these molecules are not present in the training set.