4.7 Article

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

Journal

JOURNAL OF CHEMICAL INFORMATION AND MODELING
Volume 62, Issue 20, Pages 4863-4872

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acs.jcim.2c00838

Keywords

-

Funding

  1. Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation
  2. AstraZeneca Postdoc Program

Ask authors/readers for more resources

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.
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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available