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10月27日第四十七期水木清华生命科学讲座系列
发布时间:2016-10-13关键字:

第四十七期水木清华生命科学讲座系列


 

 

  蒋华良博士,1965年生于江苏常州,1987年毕业于南京大学化学系,获得有机化学学士学位;1992年毕业于华东师范大学,获得物理化学硕士学位;1995年在中国科学院上海药物研究所获得药物化学博士学位。现任中科院上海药物所研究员、所长,新药研究国家重点实验室主任。

  蒋华良致力于药学基础研究和新药研发,他通过生物学、化学和计算科学等多学科的交叉,开展创新药物研究新策略与新方法、先导化合物发现和优化、药物靶标调控机制等研究,取得了系统性的创新成果。他担任J. Med. Chem.副主编和其他5个国际杂志顾问编委。获国家自然科学二等奖、何梁何利科技进步奖、第五届中国青年科学奖、上海市科学技术进步一等奖、上海市科技精英等多种奖项。

 

Topic: Drug Discovery by Computation

 

Speaker:

Hualiang Jiang, Ph.D.

Professor and Director

Shanghai Institute of Materia Medica , Chinese Academy of Sciences

 

Abstract:

The increasing availability and growth rate of chemical and biomedical information, also known as ‘big data’, are providing incredible opportunities for data-driven research. The exploitation of big data with information technology will not only enable us to undertake research projects never previously possible, but also stimulate a re-evaluation of all our data practices. In this sense, the data-driven drug design approaches have the potential to improve decision making in drug discovery projects, and uncovering the meaningful relationships and patterns in available data. Using big data in protein-ligand binding, in terms of both structural complexes and various binding affinities and activities, we developed a series of computational approaches to address some challenging issues on predicting protein-ligand interaction. To reduce the high attrition rate of drug discovery, we carried out extensive researches on evaluating the "drug-ability" of chemical compounds by these computational approaches. With the rapid accumulation of all types of pharmaceutical data and increasing in computer power, these computational approaches will get more and more useful for future drug design programs that can significantly improve the success rates and effectiveness of drug development.

 

日期:10月27日(周四)15:00-16:00

地点:清华大学医学科学楼B323




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