ChemistGA: A Chemical Synthesizable Accessible Molecular Generation Algorithm for Real-World Drug Discovery-Reference-Cited by-同舟云学术

ChemistGA: A Chemical Synthesizable Accessible Molecular Generation Algorithm for Real-World Drug Discovery

Published:2022-09-06 Issue:18 Volume:65 Page:12482-12496
ISSN:0022-2623
Container-title:Journal of Medicinal Chemistry
language:en
Short-container-title:J. Med. Chem.

Author:

Wang Jike¹²³,Wang Xiaorui³⁴^ORCID,Sun Huiyong⁵^ORCID,Wang Mingyang¹³,Zeng Yundian¹^ORCID,Jiang Dejun¹³,Wu Zhenxing¹,Liu Zeyi⁶,Liao Ben⁷,Yao Xiaojun⁴^ORCID,Hsieh Chang-Yu¹⁷,Cao Dongsheng⁸^ORCID,Chen Xi²,Hou Tingjun¹^ORCID

Affiliation:

1. Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China

2. School of Computer Science, Wuhan University, Wuhan 430072, Hubei, P. R. China

3. CarbonSilicon AI Technology Co., Ltd, Hangzhou 310018, Zhejiang, P. R. China

4. State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa 999078, Macau(SAR), P. R. China

5. Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing 210009, Jiangsu, P. R. China

6. DAMTP, Centre for Mathematical Sciences, University of Cambridge, Cambridge CB30WA, U.K.

7. Tencent Quantum Laboratory, Tencent, Shenzhen 518057, Guangdong, P. R. China

8. Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410004, Hunan, P. R. China

Funder

Ministry of Science and Technology of the People's Republic of China

Central University Basic Research Fund of China

Natural Science Foundation of Zhejiang Province

Publisher

American Chemical Society (ACS)

Subject

Drug Discovery,Molecular Medicine

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jmedchem.2c01179

Reference94 articles.

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2. Jannik Bjerrum, E.; Threlfall, R. Molecular Generation with Recurrent Neural Networks (RNNs), arXiv preprint. 2017, arXiv:1705.04612.

3. Erratum: Generative Recurrent Networks for De Novo Drug Design.

4. De Novo Molecule Design by Translating from Reduced Graphs to SMILES

5. An exploration strategy improves the diversity of de novo ligands using deep reinforcement learning: a case for the adenosine A2A receptor

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