Machine learning provides effective computational tools for exploring the chemical space via deep generative models. Here, we propose a new reinforcement learning scheme to fine-tune 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.

Machine learning methods have proven to be effective tools for molecular design, allowing for efficient exploration of the vast chemical space via deep molecular generative models. Here, we propose a graph-based deep generative model for de novo molecular design using reinforcement learning. We demonstrate how the reinforcement learning framework can successfully fine-tune the generative model towards molecules with various desired sets of properties, even when few molecules have the goal attributes initially. We explored the following tasks: decreasing/increasing the size of generated molecules, increasing their drug-likeness, and increasing protein-binding activity. Using our model, we are able to generate 95% predicted active compounds for a common benchmarking task, outperforming previously reported methods on this metric.

Machine learning methods have proven to be effective tools for molecular design, allowing for efficient exploration of the vast chemical space via deep molecular generative models. Here, we propose a graph-based deep generative model for de novo molecular design using reinforcement learning. We demonstrate how the reinforcement learning framework can successfully fine-tune the generative model towards molecules with various desired sets of properties, even when few molecules have the goal attributes initially. We explored the following tasks: decreasing/increasing the size of generated molecules, increasing their drug-likeness, and increasing protein-binding activity. Using our model, we are able to generate 95% predicted active compounds for a common benchmarking task, outperforming previously reported methods on this metric.