【个人简介】

Chen Hongming (陈红明) ，男，1998年于中国科学院化工冶金研究所（现过程工程研究所）获得计算化学博士学位。1998-2000年期间，在德国拜耳公司Wuppertal药物研发中心从事博士后研究工作。他在2001年加入阿斯利康制药公司瑞典哥德堡的研发中心，从事计算化学和新药开发工作长达18年，曾在先导化合物发现部门担任主任研究员（Principal Scientist）职位。他于2019-2021年期间在生物岛实验室担任研究员（Principal Investigator）并组建人工智能与药物设计研发团队，现任广州国家实验室研究员，中国生物信息与药物发现专委会副主任，入选广东省人才计划，广州黄埔精英人才。他的主要研究兴趣在计算化学，化学信息学，人工智能/机器学习等方面，已发表学术论文和专利100余篇。目前担任Molecular Informatics 杂志学术咨询委员会委员和Artificial Intelligence in the Life Sciences杂志编委委员（2018）、J. Chemoinformatics杂志编委委员（2023）。

【联系邮箱】chen_hongming@gzlab.ac.cn

【研究方向类别】

1、学术学位：药学化学

【具体研究方向】

【主要科研项目】

国家重大科技专项项目：

《基于人工智能技术的抗呼吸道病毒药物筛选及先导化合物的发现》

【所获荣誉和奖励】

【主要论文及著作（代表性）】

1. Y Lu, Q Yang, T Ran, G Zhang, W Li, P Zhou, J Tang, M Dai, J Zhong, H Chen, P He, A Zhou, B Xue, J Chen, J Zhang, S Yang, K Wu, X Wu, M Tang, W K. Zhang, D Guo, X Chen*, H Chen*, J Shang*，Discovery of orally bioavailable SARS-CoV-2 papain-like protease inhibitor as a potential treatment for COVID-19，Nature Communications，2024，15:10169.

2. H Zhang, J Huang, J Xie, W Huang, Y Yang, M Xu, J Lei, H Chen，GRELinker: A Graph-Based Generative Model for Molecular Linker Design with Reinforcement and Curriculum Learning，J Chem. Inf. Model. 2024, 10.1021/acs.jcim.3c01700

3. M Xu, H Chen, Tree-Invent: A Novel Multipurpose Molecular Generative Model Constrained with a Topological Tree, J Chem. Inf. Model. 2023, 2023, 63, 22, 7067–7082

4. Q Chen, H Sun, H Liu, Y Jiang, T Ran, X Jin, X Xiao, Z Lin, H Chen, Z Niu, An Extensive Benchmark Study on Biomedical Text Generation and Mining with ChatGPT, Bioinformatics, 2023, btad557

5. B Li, T Ran，H Chen，3D based generative PROTAC linker design with reinforcement learning，Briefings in Bioinformatics, 2023, bbad323

6. B Li, S Su, C Zhu, J Lin, X Hu, L Su, Z Yu, K Liao，H Chen，A deep learning framework for accurate reaction prediction and its application on high-throughput experimentation data，J. Cheminf. 2023, 15, 72

7. H Liu, Z Fan, J Lin, Y Yang, T Ran, H Chen，The recent progress of  deep-learning-based in silico prediction of drug combination, Drug Discovery Today，2023, 28(7):103625

8. M Tang, B Li, H Chen, Application of message passing neural networks for molecular property prediction, Current Opinion in Structural Biology, in press, 2023, 81:102616

9. Z Yu, Y Kong, B Li, S Su, J Rao, Y Gao, T Tu, H Chen, K Liao, HTE- and AI-assisted development of DHP-catalyzed decarboxylative selenation, ChemComm, 2023, 59, 2935

10. S Song，H Tan，T Ran, F Fang, L Tong, H Chen, H Xie, X Lu, Application of deep generative model for design of Pyrrolo[2,3-d] pyrimidine derivatives as new selective TANK binding kinase 1 (TBK1) inhibitors, European Journal of Medicinal Chemistry, 2023, 247, 115034

11. M Xu, W Huang, M Xu, J Lei, H Chen, 3D Conformational Generative Models for Biological Structures Using Graph Information-Embedded Relative Coordinates, Molecules, 2023, 28, 321

12. Y Tan, L Dai, W Huang, Y Guo, S Zheng, J Lei, H Chen, Y Yang, DRlinker: Deep Reinforcement Learning for Optimization in Fragment Linking Design, J Chem. Inf. Model. 2022, 62, 23, 5907–5917

13. Y. Yang, S. Zheng, S. Su, C. Zhao, J. Xu, H. Chen，SyntaLinker: Automatic Fragment Linking with Deep Conditional Transformer Neural Networks，Chemical Science, 2020, 11, 8312-8322

14. Hongming Chen, Ola Engkvist, Yinhai Wang, Marcus Olivecrona, Thomas Blaschke,The rise of deep learning in drug discovery, Drug discovery today, 2018,6,1241-1250

15. Thomas Blaschke, Josep Arús-Pous, Hongming Chen, Christian Margreitter, Christian Tyrchan, Ola Engkvist, Kostas Papadopoulos, Atanas Patronov,REINVENT 2.0: an AI tool for de novo drug design, Journal of chemical information and modeling,2020,10,5918-5922

16. Oleksii Prykhodko, Simon Viet Johansson, Panagiotis-Christos Kotsias, Josep Arús-Pous, Esben Jannik Bjerrum, Ola Engkvist, Hongming Chen,A de novo molecular generation method using latent vector based generative adversarial network,Journal of Cheminformatics,2019,12,1-13