Synthetically Feasible De Novo Molecular Design of Leads Based on a Reinforcement Learning Model: AI-Assisted Discovery of an Anti-IBD Lead Targeting CXCR4-Reference-Cited by-同舟云学术

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Synthetically Feasible De Novo Molecular Design of Leads Based on a Reinforcement Learning Model: AI-Assisted Discovery of an Anti-IBD Lead Targeting CXCR4

Published:2024-06-12 Issue:12 Volume:67 Page:10057-10075
ISSN:0022-2623
Container-title:Journal of Medicinal Chemistry
language:en
Short-container-title:J. Med. Chem.

Author:

Jiang Xiaoying¹²,Lu Liuxin¹²,Li Junjie¹²,Jiang Jing³,Zhang Jiapeng⁴,Zhou Shengbin⁴,Wen Hao¹²,Cai Hong¹²,Luo Xinyu¹²,Li Zhen³,Wang Jiahui¹²,Ju Bin³,Bai Renren¹²^ORCID

Affiliation:

1. School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, PR China

2. Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines; Engineering Laboratory of Development and Application of Traditional Chinese Medicines; Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, Hangzhou Normal University, Hangzhou 311121, PR China

3. SanOmics AI Co. Ltd., Hangzhou 311103, PR China

4. School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, PR China

Funder

Hangzhou Normal University

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jmedchem.4c00184

Reference47 articles.

1. Artificial intelligence in drug discovery and development

2. De novo molecular design and generative models

3. De novo molecular drug design benchmarking

4. Leveraging molecular structure and bioactivity with chemical language models for de novo drug design

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