Improving drug discovery with a hybrid deep generative model using reinforcement learning trained on a Bayesian docking approximation-Reference-Cited by-同舟云学术

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Improving drug discovery with a hybrid deep generative model using reinforcement learning trained on a Bayesian docking approximation

Published:2023-08-08 Issue:11 Volume:37 Page:507-517
ISSN:0920-654X
Container-title:Journal of Computer-Aided Molecular Design
language:en
Short-container-title:J Comput Aided Mol Des

Author:

Xiong Youjin,Wang Yiqing,Wang Yisheng,Li Chenmei,Yusong Peng,Wu Junyu,Wang Yiqing,Gu Lingyun,Butch Christopher J.

Publisher

Springer Science and Business Media LLC

Subject

Physical and Theoretical Chemistry,Computer Science Applications,Drug Discovery

Link

https://link.springer.com/content/pdf/10.1007/s10822-023-00523-3.pdf

Reference57 articles.

1. Lyu J, Irwin JJ, Shoichet BK (2023) Modeling the expansion of virtual screening libraries. Nat Chem Biol 19:712–718. https://doi.org/10.1038/s41589-022-01234-w

2. Lyu J, Wang S, Balius TE et al (2019) Ultra-large library docking for discovering new chemotypes. Nature 566:224–229. https://doi.org/10.1038/s41586-019-0917-9

3. Irwin JJ, Tang KG, Young J et al (2020) ZINC20—a free ultralarge-scale chemical database for ligand discovery. J Chem Inf Model 60:6065–6073

4. Shivanyuk AN, Ryabukhin SV, Tolmachev A et al (2007) Enamine real database: making chemical diversity real. Chemistry today 25:58–59

5. Varela-Rial A, Majewski M, De Fabritiis G (2022) Structure based virtual screening: Fast and slow. WIREs Comput Mol Sci 12:e1544. https://doi.org/10.1002/wcms.1544

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