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李卫华
发布时间:2014-02-17   访问次数:23653   作者:


李卫华      教授   博士生导师


Email:whli.at.ecust.edu.cn(用@替换.at.)


【个人简介】

1999年毕业于安徽师范大学化学系,获学士学位;2002年毕业于华南师范大学化学系,获硕士学位;2005年毕业于中国科学院上海药物研究所,获博士学位。2005年9月至2007年6月在raybet雷竞技竞猜在线官网从事博士后研究;2007年7月至2009年6月获日本学术振兴会(JSPS)奖学金,在日本千叶大学药学部从事博士后研究。2009年9月到raybet雷竞技竞猜在线官网工作,历任副研究员,教授。

  

【研究方向】

1)蛋白质/酶的计算模拟和药物设计

2)人工智能方法在药物发现和设计中的应用


主要从事蛋白质/酶的计算模拟和计算机辅助药物设计研究工作。运用计算模拟和人工智能技术,围绕P450酶介导的药物代谢、化合物ADMET性质预测、核受体的药物发现和设计等方面开展研究。研究工作先后发表于J Chem Inf Model, J Chem Theory Comput, Chem Eur J, Mol Pharm, Chem Res Toxicol, Drug Metab Dispos期刊。作为主持人先后承担国家自然科学基金、上海市自然科学基金等科研项目;作为项目骨干参与国家重点研发计划课题新药创制重大专项课题

  

【近期主要论文】


1)  Yanjun Feng, Changda Gong, Jieyu Zhu, Guixia Liu, Yun Tang*, and Weihua Li*. Unraveling the ligand-binding sites of CYP3A4 by molecular dynamics simulations with solvent probes. J. Chem. Inf. Model. 2024, 64, 3451−3464.


2Yanjun Feng, Changda Gong, Jieyu Zhu, Guixia Liu, Yun Tang*, and Weihua Li*. Prediction of sites of metabolism of CYP3A4 substrates utilizing docking-derived geometric features. J. Chem. Inf. Model. 2023,  63, 4158-4169.


3)  Minjie Xu, Zhou Lu, Zengrui Wu, Minyan Gui, Guixia Liu, Yun Tang*, and Weihua Li*. Development of in silico models for predicting potential time-dependent inhibitors of cytochrome P450 3A4. Mol. Pharmaceut. 2023, 20, 194-205.


4) Longqiang Li, Zhou Lu, Guixia Liu, Yun Tang, and Weihua Li*. Machine learning models to predict cytochrome P450 2B6 inhibitors and substrates. Chem. Res. Toxicol. 2023, 36, 1332-1344.


5)  Longqiang Li, Zhou Lu, Guixia Liu, Yun Tang, and Weihua Li*. In silico prediction of human and rat liver microsomal stability via machine learning methods. Chem. Res. Toxicol. 2022, 35, 1614−1624.


6)  Minjie Xu, Hongbin Yang, Guixia Liu, Yun Tang*, and Weihua Li*. In silico prediction of chemical aquatic toxicity by multiple machine learning and deep learning approaches. J. Appl. Toxicol. 2022, 42,1766- 1776.


7)  Xiaoxiao Zhang, Piaopiao Zhao, Zhiyuan Wang, Xuan Xu, Guixia Liu, Yun Tang, and Weihua Li*. In silico prediction of CYP2C8 inhibition with machine learning methods. Chem. Res. Toxicol.2021, 34, 1850-1859.


8)  Xiaoxiao Zhang, Minjie Xu, Zengrui Wu, Guixia Liu, Yun Tang, and Weihua Li*. Assessment of CYP2C9 structural models for site of metabolism prediction. ChemMedChem 2021, 16, 1754-1763.


9)  Junhao Li, Yue Chen, Yun Tang, Weihua Li*, and Yaoquan Tu*. Homotropic cooperativity of midazolam metabolism by cytochrome P450 3A4: Insight from computational studies. J. Chem. Inf. Model. 2021, 61, 2418-2426.


10) Junhao Li, Yang Zhou, Yun Tang, Weihua Li*, and Yaoquan Tu*. Dissecting the structural plasticity and dynamics of cytochrome P450 2B4 by molecular dynamics simulations. J. Chem. Inf. Model.  2020, 60, 5026-5035.


11) Yue Chen, Junhao Li, Zengrui Wu, Guixia Liu, Honglin Li, Yun Tang*, and Weihua Li*. Computational insight into the allosteric activation mechanism of farnesoid X receptor.  J. Chem. Inf. Model. 2020, 60, 1540-1550.


12) Junhao Li, Yun Tang, Weihua Li*, and Yaoquan Tu*. Mechanistic insights into the regio- and stereoselectivities of testosterone and dihydrotestosterone hydroxylation catalyzed by CYP3A4 and CYP19A1. Chem. Eur. J. 2020, 26, 6214-6223.


13) Yuhan Xue, Junhao Li, Zengrui Wu, Guixia Liu, Yun Tang, and Weihua Li*. Computational insights into the different catalytic activities of CYP3A4 and CYP3A5 towards schisantherin E. Chem. Biol. Drug Des. 2019, 93, 854-864.


14) Junhao Li, Hongxiao Zhang, Guixia Liu, Yun Tang, Yaoquan Tu* and Weihua Li*. Computational insight into vitamin K1 ω-hydroxylation by cytochrome P450 4F2. Front. Pharmacol. 2018, 9, 1065.


15) Yue Chen, Hongbin Yang, Zengrui Wu, Guixia Liu, Yun Tang, and Weihua Li*. Prediction of Farnesoid X receptor disruptors with machine learning methods. Chem. Res. Toxicol. 2018, 31, 1128-1137.


16) Hanwen Du, Junhao Li, Yingchun Cai, Hongxiao Zhang, Guixia Liu, Yun Tang*, and Weihua Li*. Computational investigation of ligand binding to the peripheral site in CYP3A4: Conformational dynamics and inhibitor discovery. J. Chem. Inf. Model. 2017, 57, 616-626.


17) Hanwen Du, Yingchun Cai, Hongbing Yang, Hongxiao Zhang, Yuhan Xue, Guixia Liu, Yun Tang, and Weihua Li*. In silico prediction of chemicals binding to aromatase with machine learning methods. Chem. Res. Toxicol. 2017, 30, 1209-1218.


18)  Weihua Li, Jing Fu, Feixiong Cheng, Mingyue Zheng, Jian Zhang, Guixia Liu, and Yun Tang. Unbinding pathways of GW4064 from human farnesoid X receptor as revealed by molecular dynamics simulations. J. Chem. Inf. Model. 2012, 52, 3043-3052.


19)  Feixiong Cheng, Yue Yu, Yadi Zhou, Zhonghua Shen, Wen Xiao, Guixia Liu, Weihua Li*, Philip W. Lee, and Yun Tang*. Insights into molecular basis of cytochrome P450 inhibitory promiscuity of compounds. J. Chem. Inf. Model. 2011, 51, 2482-2495.


20)  Feixiong Cheng, Yue Yu, Jie Shen, Lei Yang, Weihua Li*, Guixia Liu, Philip W. Lee, and Yun Tang*. Classification of cytochrome P450 inhibitors and noninhibitors using combined classifiers. J. Chem. Inf. Model. 2011, 51, 996-1011.


21) Jie Shen, Feixiong Cheng, You Xu, Weihua Li*, and Yun Tang*. Estimation of ADME properties with substructure pattern recognition. J. Chem. Inf. Model. 2010, 50, 1034-1041.



其它论文:

https://pubmed.ncbi.nlm.nih.gov/?term=%22Li%20Weihua%22%5Bau%5D%20and%20%22School%20of%20pharmacy%22%20and%20%22East%20China%20University%22%20%5Bad%5D&sort=date