胡华彬
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胡华彬博士,大连理工大学医学部药学院副教授,硕/博士生导师。2014年获得哈尔滨医科大学学士学位,2017年获得北京协和医学院-清华大学医学部硕士学位,并于2021年获得德国波恩大学博士学位,师从化学信息学领域著名学者Jürgen Bajorath教授。博士毕业后,申请人先后在英国癌症研究所及瑞典乌普萨拉大学开展博士后研究,师从Albert A. Antolin教授和Jens Carlsson教授。该课题组长期致力于AI驱动的原创性药物分子设计研究。在相关研究领域,共发表35篇SCI论文,其中以第一作者或通讯作者身份在JACS Au、Cell Chemical Biology、Journal of Medicinal Chemistry等期刊发表25篇文章。此外,作为共同作者,其研究成果亦发表于Science Advances、Nucleic Acids Research等国际著名期刊。
研究方向
🔬AI+药物设计
ü 运用AI算法加速超大规模虚拟筛选
ü 针对癌症等重大疾病靶点的原创药物设计
特色:"干湿实验闭环"——计算预测与实验验证紧密结合
关键词:AI赋能药物设计 · 虚拟筛选 · 干湿实验结果的交叉验证
教育背景
2017/10 ~ 2021/07 德国波恩大学 化学信息学 博士
2014/09 ~ 2017/07 北京协和医学院-清华大学医学部 药物化学 硕士
2010/09 ~2014/06 哈尔滨医科大学 药物制剂 学士
工作经历
2025/03 ~ 至今 大连理工大学 副教授
2022/09 ~ 2024/09 瑞典乌普萨拉大学 博士后(计算化学)
2021/11 ~ 2022/09 英国伦敦癌症研究所,ICR 博士后(化学信息学与药物设计)
代表性科研成果(+ co-first author; * corresponding author)
1. Hu, H.*; Yi, X.; Xue, L.; Jonathan B. Baell*. A collection of useful nuisance compounds (CONS) for interrogation of bioassay integrity. JACS Au 2024, 4 (12), 4883–4891.
2. Feng, B.; Yu, H.; Dong, Y.; Díaz-Holguín, A; Hu, H*. Identification of bioactive compounds with popular single-atom modifications: Comprehensive analysis and implications for compound design. European Journal of Medicinal Chemistry 2025, 283, 117051.
3. Feng, B.; Yu, H.; Dong, Y.; Díaz-Holguín, A; Antolin, A. A.; Hu, H*. Combining data-driven and structure-based approaches in designing dual PARP1-BRD4 inhibitors for breast cancer treatment. Journal of Chemical Information and Modeling 2024, 64 (19), 7725–7742.
4. Feng, B.; Zhang, J.; Liu, Z.; Xu, Y. *; Hu, H*. Discovery and biological evaluation of novel dual PTP1B and ACP1 inhibitors for the treatment of insulin resistance. Bioorganic & Medicinal Chemistry 2024, 97, 117545.
5. Hu, H.; Serra, C.; Zhang, W.; Scrivo, A.; Fernández-Carasa, I.; Consiglio, A.; Aytes, A.; Pujana, M. A.; Llebaria, A.*; Antolin, A. A*. Identification of differential biological activity and synergy between the PARP inhibitor rucaparib and its major metabolite. Cell Chemical Biology 2024, 31 (5), 973-988.e974.
6. Hu, H.+; Tjaden, A.+; Knapp, S.; Antolin, A. A.; Müller S. A machine learning and live-cell imaging tool kit uncovers small molecules induced phospholipidosis. Cell Chemical Biology 2023, 30 (12), 1634-1651.e1636.
7. Feng, B.+; Hu, H.+; Xiang, J.+; Wu, G.; Zhu, Z.; Zhang, G.; Zhang, J.; Pan, W.; Zhang, W.; Li, T.; Wu, S. Discovery of novel triphenylphosphonium conjugated pleuromutilin with enhanced anti-MRSA effect and broaden antibacterial spectrum. European Journal of Medicinal Chemistry 2025, 117731.
8. Laufkotter, O.+; Hu, H.+; Miljkovic, F.; Bajorath, J. Structure- and similarity-based survey of allosteric kinase inhibitors, activators, and closely related compounds. Journal of Medicinal Chemistry 2022, 65 (2), 922-934.
9. Hu, H.+; Laufkotter, O.+; Miljkovic, F.; Bajorath, J. Systematic comparison of competitive and allosteric kinase inhibitors reveals common structural characteristics. European Journal of Medicinal Chemistry 2021, 214, 113206.
10. Hu, H.; Bajorath, J. Activity cliffs produced by single-atom modification of active compounds: Systematic identification and rationalization based on X-ray structures. European Journal of Medicinal Chemistry 2020, 207, 112846.
11. Hu, H.; Bajorath, J. Systematic Exploration of Activity Cliffs Containing Privileged Substructures. Molecular Pharmaceutics 2020, 17 (3), 979-989.
12. Díaz-Holguín, A., Saarinen, M., Vo, D.D., Sturchio, A., Branzell, N., Cabeza de Vaca, I., Hu, H., Mitjavila-Domènech, N., Lindqvist, A., Baranczewski, P.; Millan, M. J.; Yang ,Y.; Kihlberg, J.; Carlsson, J.; Svenningsson, P. AlphaFold accelerated discovery of psychotropic agonists targeting the trace amine-associated receptor 1. Science Advances 2024, 10(32):eadn1524.
13. di Micco, P.; Antolin, A. A.; Mitsopoulos, C.; Villasclaras-Fernandez, E.; Sanfelice, D.; Dolciami, D.; Ramagiri, P.; Mica, I. L.; Tym, J. E.; Gingrich, P. W.; Hu, H.; Workman, P.; Al-Lazikani, B. canSAR: update to the cancer translational research and drug discovery knowledgebase. Nucleic Acids Research 2023, 51 (D1), D1212-D1219.
✨ 课题组文化:在AI与药物的交叉地带,做有趣且有价值的研究
本课题组长期招聘计算化学、药物化学及相关研究方向的同学,尤其欢迎对超大规模虚拟筛选、人工智能驱动的药物设计感兴趣的研究生和科研助理加入!