Optimization of binding affinities in chemical space with generative pre-trained transformer and deep reinforcement learning [version 2; peer review: 2 approved, 3 approved with reservations]

Optimization of binding affinities in chemical space with generative pre-trained transformer and deep reinforcement learning [version 2; peer review: 2 approved, 3 approved with reservations]

Background The key challenge in drug discovery is to discover novel compounds with desirable properties. Among the properties, binding affinity to a target is one of the prerequisites and usually evaluated by molecular docking or quantitative structure activity relationship (QSAR) models. Methods In...

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Bibliographic Details
Main Authors: Xiaopeng Xu, Juexiao Zhou, Xin Gao, Yu Wang, Xingyu Liao, Zhongxiao Li, Ruochi Zhang, Chen Zhu, Qing Zhan
Format: Article
Language:English
Published: F1000 Research Ltd 2024-02-01
Series:F1000Research
Subjects:
Drug design
transformers
reinforcement learning
molecular docking
hit discovery
eng
Online Access:https://f1000research.com/articles/12-757/v2

Internet

https://f1000research.com/articles/12-757/v2

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