【作者】 郑明月； 蒋华良； 陈凯先；

【Author】 Mingyue Zheng;Hualiang Jiang;Kaixian Chen;Shanghai Institute of Materia Medica,Chinese Academy of Sciences;School of Life Science and Technology,Shanghai Tech University;

【机构】 中国科学院上海药物研究所药物发现与设计中心； 上海科技大学生命科学与技术学院；

【摘要】 当前,各种生物医药数据以前所未有的速度增长,如何更好的利用这些数据和信息,深入理解药物作用的复杂生物过程,提高先导化合物发现的效率,并提升候选化合物的安全性和有效性,是目前和今后创新药物研发的重要研究内容。我们主要围绕药物靶标-配体相互作用评价、药物靶标预测以及早期ADME性质和毒副作用评价等方面,开展数据驱动的药物设计方法学发展和应用研究。随着数据积累、计算水平及分析手段的不断发展,基于大规模数据的药物设计方法有望成为创新药物研究快速发展的关键推动力之一。更多还原

【Abstract】 The increasing availability and growth rate of chemical and biomedical information 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 knowledge-based 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 data-driven approaches.With the rapid accumulation of all types of pharmaceutical data and increasing in computer power,these data-driven approaches will get more and more useful for future drug design programs that can significantly improve the success rates and effectiveness of drug development.更多还原