Augmented Hill-Climb increases reinforcement learning efficiency for language-based de novo molecule generation.

ABSTRACT: A plethora of AI-based techniques now exists to conduct de novo molecule generation that can devise molecules conditioned towards a particular endpoint in the context of drug design. One popular approach is using reinforcement learning to update a recurrent neural network or language-based de novo molecule generator. However, reinforcement learning can be inefficient, sometimes requiring up to 10⁵ molecules to be sampled to optimize more complex objectives, which poses a limitation when using computationally expensive scoring functions like docking or computer-aided synthesis planning models. In this work, we propose a reinforcement learning strategy called Augmented Hill-Climb based on a simple, hypothesis-driven hybrid between REINVENT and Hill-Climb that improves sample-effic

SUBMITTER: Thomas M 

PROVIDER: S-EPMC9531503 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature