Renxiao Wang Ph.D.
Professor
Department of Medicinal Chemistry, School of Pharmaceutical Sciences, Fudan University
826 Zhangheng Road, Shanghai 201203, People's Republic of China
E-mail: wangrx@fudan.edu.cn
BIOGRAPHY
Prof. Renxiao Wang received his Ph.D. at Peking University in 1999. He did his postdoctoral training at the University of California Los Angeles and Georgetown University from 1999 to 2001, and then worked as a research investigator at University of Michigan Medical School from 2001 to 2005. In 2005, he joined Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, as a full professor and principal investigator. In 2020, he started to work at the Pharmacy School at Fudan University.
Prof. Wang’s research interests focus on developing new computational methods for molecular-targeted drug design and understanding how small organic molecules regulate their biological targets through molecular modeling. Prof. Wang has published over 130 scientific papers in peer-reviewed journals with a H-index of 35. In 2012, he received the Corwin Hansch Award from the International Cheminformatics & QSAR Society for his outstanding contributions to this field. Prof. Wang now serves as an associate editor for ACS's Journal of Chemical Information and Modeling and an editorial board member for several other professional journals.
1.Develop computational methods applicable to molecular-targeted drug discovery.
2.Understand how small organic molecules regulate their biological targets through molecular modeling.
3.Discover small-molecule regulators of protein-protein interactions with pharmaceutical implication through a combination of molecular modeling, organic synthesis and biological experiments.
Education
1994-1999: Ph.D., College of Chemistry, Peking University, Beijing, China
1989-1994: B.S., College of Chemistry, Peking University, Beijing, China
Professional Experiences
2020–now: Professor, principal investigator, School of Pharmaceutical Sciences, Fudan University
2005–2019: Professor, principal investigator, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, P. R. China
2001–2005: Research investigator, Department of Internal Medicine, University of Michigan Medical School, U.S.A.
2000–2001: Postdoctoral fellow, Lombardi Cancer Center, Georgetown University, Washington D.C., U.S.A.
1999-2000: Postdoctoral fellow, Department of Chemistry and Biochemistry, University of California, Los Angeles, California, U.S.A
Research Interests and Achievements
Prof. Wang’s major research interests include: (1) Study and development of new methods for AI-driven drug design. (2) Identify novel potential targets related to major diseases, apply molecular design technologies to discover and optimize lead compounds, and develop innovative drugs.
Prof. Wang has published more than 150 SCI-indexed papers (with over 18,000 citations recorded on Google Scholar and an H-index of 52), and has been granted more than 40 patents and software copyrights. He has been consistently named the Elsevier Highly Cited Chinese Researchers for multiple years. His representative achievements include research on scoring functions for protein-ligand interactions, the development of protein-ligand complex databases, and automated design methods for ligand molecules. In practice, he has also applied drug design technologies to target protein-protein interaction systems such as the Bcl-2 family, successfully obtaining several classes of anti-tumor lead compounds. As the principal investigator, he has undertaken several key research projects, including those supported by the National Natural Science Foundation of China, the National Key R&D Program of China, and the Shanghai Municipal Science and Technology Commission.
Prof. Wang has received several prestigious domestic awards, including the WuXi AppTec Life Chemistry Research Award, the Young Computational Chemist Award from the Chinese Chemical Society, and the Servier Young Medicinal Chemist Award from the Chinese Pharmaceutical Association. In 2012, he was honored with the Corwin Hansch Award from the Cheminformatics and QSAR Society, becoming the first Chinese scientist to receive this international accolade. He also serves as Vice Chair of the Computer Chemistry Committee of the Chinese Chemical Society, and as a member of the Chemical Biology Committee of the Chinese Chemical Society, the Drug Discovery Committee of the Chinese Society of Bioinformatics, the Medicinal Chemistry Committee of the Shanghai Pharmaceutical Association, and the Intelligent Pharmaceuticals Committee of the Shanghai Pharmaceutical Association. In 2017, he was awarded the National Science Fund for Distinguished Young Scholars by the Department of Medical Sciences of the National Natural Science Foundation of China.
Awards & Honors
Young Computational Chemist Award, Chinese Chemical Society (2017)
Corwin Hansch Award, Chemoinformatics & QSAR Society (2012)
Biological & Chemical Research Excellency Award, Wuxi AppTech. (2010)
SCOPUS Young Researcher Momentum Award, Pfizer & Elsevier (2010)
Sevier Young Scientist Award, Chinese Pharmaceutical Association (2009)
Memberships
Member of the Chinese Chemical Society, the Chinese Pharmaceutical Association, and the American Chemical Society.
Associate editor of Journal of Chemical Information and Modeling (ACS)
Editorial board members of ChemMedChem (Wiley), Molecular Informatics (Wiley), Journal of Chinese Pharmaceutical Sciences, & Chinese Journal of Chemistry.
Selected Publications
(1) Chen, Z. H.; Wang, H. J.; Wang, R. X.;* Qi, Y. F.*, “ThermoSeek: An Integrated Web Resource for Sequence and Structural Analysis of Proteins from Thermophilic Species”, J. Chem. Inf. Model., ASAP, DOI: 10.1021/acs.jcim.5c00010
(2) Zhang, Z.; Gao, R. Y.; Zhao, M. L.; Zhang, X. Y.; Gao, H. T.; Qi, Y. F.; Wang, R. X.;* Li, Yan* “Computational Methods for Predicting Chemical Reactivity of Covalent Compounds” J. Chem. Inf. Model. 2025, 65, 1140-1154. DOI: 10.1021/acs.jcim.4c01591
(3) Zhang, X. Y.; Gao, H. T.; Qi, Y. F.; Li, Y.;* Wang, R. X.* “Generation of Rational Drug-like Molecular Structures Through a Multiple-Objective Reinforcement Learning Framework”, Molecules 2025, 30(1), 18; DOI: 10.3390/molecules30010018.
(4) Chen, Z. H.; Ji, M. L.; Qian, J.; Zhang, Z.; Zhang, X. Y.; Wang, H. J.; Gao, H. T.; Wang, R. X.*; Qi, Y. F.* “ProBID-Net: A Deep Learning Model for Protein-Protein Binding Interface Design”, Chem. Sci., 2024, 15, 19977-19990. DOI: 10.1039/D4SC02233E
(5) Gong, Q. N.; Li, C. P.; Wang, H. J.; Cao, J. R.; Li, Z.; Zhou, M.; Li, Y.; Chu, Y.*; Liu, H.*; Wang, R. X.* “Discovery of Phenylpyrazole Derivatives as a New Class of Selective Inhibitors of MCL-1 with Anti-Tumor Activity”, ACS Omega 2024, 9(25), 27369–27396. DOI: 10.1021/acsomega.4c02021
(6) Zhang, X. Y.; Gao, H. T.; Wang, H. J.; Chen, Z. H.; Zhang, Z.; Chen, X. C.; Li, Y.; Qi, Y. F.*; Wang, R. X.* “PLANET: A Multi-objective Graph Neural Network Model for Protein-Ligand Binding Affinity Prediction”, J. Chem. Inf. Model., 2024, 64(7), 2205–2220. DOI: 10.1021/acs.jcim.3c00253.
(7) Gong, Q. N.; Wang, H. J.; Zhou, M.; Zhou, L.*; Wang, R. X.*; Li, Y.* “B-cell lymphoma-2 family proteins in the crosshairs: Small molecule inhibitors and activators for cancer therapy”, Med. Res. Rev., 2024; 44, 707-737. DOI:10.1002/med.21999
(8) Xiang, H. G.; Zhou, M.; Li, Y.; Zhou, L.*; Wang, R. X.* “Drug discovery by targeting the protein‒protein interactions involved in autophagy”, Acta Pharm. Sinica B, 2023, 13(11), 4373-4390. DOI: 10.1016/j.apsb.2023.07.016
(9) Xiang, H.; Liu, R.; Zhang, X.; An, R.; Zhou, M.; Tan, C.; Li, Q.; Su, M.; Guo, C.; Zhou, L.;* Li, Y.;* Wang, R. X.* Discovery of Small-Molecule Autophagy Inhibitors by Disrupting the Protein-Protein Interactions Involving ATG5, J. Med. Chem. 2023, 66(4), 2457–2476. DOI: 10.1021/acs.jmedchem.2c01233
(10) Qiu, X. X.; Li, N.; Yang, Q. F.; Wu, S.; Li, X. H.; Pan, X. H.; Yamamoto, S.; Zhang; X. Z.; Zeng, J. C.; Liao, J. H.; He, C. C.; Wang, R. X.*; Zhao, Y. X.* “The potent BECN2-ATG14 coiled-coil interaction is selectively critical for endolysosomal degradation of GPRASP1/GASP1-associated GPCRs”, Autophagy, 2023, 19(11), 2884-2898. DOI: 10.1080/15548627.2023.2233872
(11) Li, Y.*; Zhang, Z.; Wang, R. X.* “HydraMap v.2: Prediction of Hydration Sites and Desolvation Energy with Refined Statistical Potentials”, J. Chem. Inf. Model., 2023, 63(15), 4749–4761; DOI: 10.1021/acs.jcim.3c00408.
(12) Feng, G. Q.; Zhang, X. Y.; Li, Y.*; Wang, R. X.* “Analysis of the Binding Sites on BAX and the Mechanism of BAX Activators Through Extensive Molecular Dynamics Simulations”, J. Chem. Inf. Model. 2022, 62(21), 5208−5222. DOI: 10.1021/acs.jcim.0c01420
(13) Li, Y.; Fan, W. J.; Gong, Q. N.; Tian, J.; Zhou, M.; Li, Q.; Uwituze, L. B.; Zhang, Z. C.*; Hong, R.*; Wang, R. X.* “Structure-Based Optimization of 3-phenyl-N-(2-(3-phenylureido)ethyl) thiophene-2-sulfonamide Derivatives as Selective Mcl-1 Inhibitors”, J. Med. Chem. 2021, 64(14),10260-10285. DOI: 10.1021/acs.jmedchem.1c00690
(14) Yang, Q. F.; Qiu, X. X.; Zhang, X. Z.; Yu, Y. T.; Li, N.; Wei, X.; Feng, G. Q.; Li, Y.; Zhao, Y. X.*; Wang, R. X.* “Optimization of Beclin 1-Targeting Stapled Peptides by Staple Scanning Leads to Enhanced Antiproliferative Potency in Cancer Cells”, J. Med. Chem. 2021, 64(18), 13475–13486. DOI: 10.1021/acs.jmedchem.1c00870
(15) Du, Y.; Wang, R. X.* “Revealing the Unbinding Kinetics and Mechanism of Type I and Type II Protein Kinase Inhibitors by Local-Scaled Molecular Dynamic Simulations”, J. Chem. Theory Comput. 2020, 16(10), 6620−6632. dx.doi.org/10.1021/acs.jctc.0c00342.
(16) Li, Y.; * Gao, Y. D.; Holloway, M. K,; Wang, R. X.* “Integration of Desolvation Effect into an Empirical Scoring Function”, J. Chem. Inf. Model., 2020, 60(9), 4359−4375. DOI：10.1021/acs.jcim.9b00619.
(17) Li, Y.;* Sun, Y. P.; Song, Y. P.; Dai, D. C.; Zhao, Z. X.; Zhang, Q.; Zhong, W. G.; Hu, L. Y.; Ma, Y. L.; Li, X.;* Wang, R. X.* “Fragment-Based Computational Method for Designing GPCR Ligands”, J. Chem. Inf. Model. 2020, 60(9), 4339−4349. doi.org/10.1021/acs.jcim.9b00699.
(18) Su, M. Y.; Feng, G. Q.; Liu, Z. H.; Li, Y.; Wang, R. X.* “Tapping on the Black Box: How is the Scoring Power of a Machine-Learning Scoring Function Depended on the Training Set?”, J. Chem. Inf. Model. 2020, 60(3), 1122-1136. doi.org/10.1021/ acs.jcim.9b00714
(19) Yang, Q. F.; Su, M. Y.; Li, Y.; Wang, R. X.* “Revisiting the Relationship Between Correlation Coefficient, Confidence Level, and Sample Size”, J. Chem. Inf. Model. 2019, 59(11), 4602-4612.
(20) Su, M. Y.; Yang, Q. F.; Du, Y.; Feng, G. Q.; Liu, Z. H.; Li, Y.;* Wang, R. X.* “Comparative Assessment of Scoring Functions: The CASF-2016 Update”, J. Chem. Inf. Model. 2019, 59, 895−913.
(21) Li, Y.; Su, M. Y.; Liu, Z. H.; Li, J.; Liu, J.; Han, L.; Wang, R. X.* Assessing Protein-Ligand Interaction Scoring Functions with the CASF-2013 Benchmark, Nat. Protocol., 2018, 13, 666-680. doi:10.1038/ nprot.2017.114.
(22) Qu, Y.-Q.; Gordillo-Martinez, F.; Law, B. Y. K.; Han, Y. Wu, A.-G.; Zeng, W.; Lam, W.-K.; Ho, C.; Mok, S. W. F.; He, H.-Q.; Wong, V. K. W.*; Wang, R. X.* 2-Aminoethoxydiphenylborane sensitizes anti-tumor effect of bortezomib via suppression of calcium-mediated autophagy, Cell Death & Disease, 2018, 9, 361. DOI:10.1038/s41419-018-0397-0.
(23) Liu, Z. H.; Su, M. Y.; Han, Li.; Liu, J.; Yang, Q. F.; Li, Y.;* Wang, R.-X.* Forging the Basis for Developing Protein−Ligand Interaction Scoring Functions, Acc. Chem. Res. 2017, 50(2), 302–309.
