【國重報告】基于靶點結構的虛擬篩選中個性化策略的探討

发布日期:2021-01-13     浏览次数:次   

報告題目:基于靶點結構的虛擬篩選中個性化策略的探討

報告人:侯廷军教授, 浙江大学

時間:2021年01月18日10:00

地點:曾呈奎樓3樓B311

報告摘要:

By taking advantage of the high-performance computers, docking-based virtual screening (VS) has gained more and more attention, and has become one of the core technologies in drug design and development. However, the prediction accuracy of molecular docking may be impaired by some inherent defects, such as simplified scoring functions and ignorance of protein flexibility. In this talk, I will discuss some new strategies developed in my group to improve the efficiency and accuracy of docking-based virtual screening. In the first part, I will discuss the MIEC-SVM approach based on free energy decomposition and machine learning algorithm, which shows good capability to identify binding peptides of modular domains and small molecule inhibitors of drug targets. In the second part, I will discuss a novel parallel virtual screening strategy by integrating molecular docking and complex-based pharmacophore searching based on multiple protein structures.

報告人簡介:

浙江大學藥學院求是特聘教授,藥物信息和計算生物學中心主任。長期圍繞計算機輔助藥物設計中的核心問題展開前沿交叉學科研究,在Chemical Reviews, ACS Central Science, Nucleic Acids Research, PNAS, Briefings in BioinformaticsJournal of Medicinal Chemistry, Journal of Chemical Theory and Computation, Journal of Chemical Information and Modeling知名期刊发表SCI论文350余篇,SCI引用12000余次,H因子60;获授權專利和软件著作权28项。任中国化学会计算(机)化学专业委员会副主任委员兼秘书长,J Cheminf、J Chem Inf Model、Int J Mol Sci等14种SCI期刊编委或顾问编委。


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