Data-driven medicinal chemistry in the era of big data

被引：87
|
作者：
Lusher, Scott J. [2 ]
McGuire, Ross [2 ,3 ]
van Schaik, Rene C. [1 ]
Nicholson, C. David [4 ]
de Vlieg, Jacob [1 ,2 ]
机构：
[1] Netherlands eSci Ctr, Amsterdam, Netherlands
[2] Radboud Univ Nijmegen, Computat Drug Discovery Grp, NL-6525 ED Nijmegen, Netherlands
[3] Bioaxis Res, Oss, Netherlands
[4] Bayer Crop Sci, Monheim, Germany
来源：
DRUG DISCOVERY TODAY | 2014年 / 19卷 / 07期
关键词：
PROTEIN DATA-BANK; DRUG DISCOVERY; BIOISOSTERIC REPLACEMENTS; LIGAND EFFICIENCY; LEAD OPTIMIZATION; SMALL MOLECULES; DESIGN; DATABASES; KNOWLEDGE; QUALITY;
D O I：
10.1016/j.drudis.2013.12.004
中图分类号：
R9 [药学];
学科分类号：
1007 ;
摘要：
Science, and the way we undertake research, is changing. The increasing rate of data generation across all scientific disciplines is providing incredible opportunities for data-driven research, with the potential to transform our current practices. The exploitation of so-called 'big data' will enable us to undertake research projects never previously possible but should also stimulate a re-evaluation of all our data practices. Data-driven medicinal chemistry approaches have the potential to improve decision making in drug discovery projects, providing that all researchers embrace the role of 'data scientist' and uncover the meaningful relationships and patterns in available data.
引用
收藏
页码：859 / 868
页数：10
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