ClickGen: Directed exploration of synthesizable chemical space via modular reactions and reinforcement learning.

ABSTRACT: Despite the significant potential of generative models, low synthesizability of many generated molecules limits their real-world applications. In response to this issue, we develop ClickGen, a deep learning model that utilizes modular reactions like click chemistry to assemble molecules and incorporates reinforcement learning along with inpainting technique to ensure that the proposed molecules display high diversity, novelty and strong binding tendency. ClickGen demonstrates superior performance over the other reaction-based generative models in terms of novelty, synthesizability, and docking conformation similarity for existing binders targeting the three proteins. We then proceeded to conduct wet-lab validation on the ClickGen's proposed molecules for poly adenosine diphosphate-ribose p

SUBMITTER: Wang M 

PROVIDER: S-EPMC11584676 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature