Bias, reporting, and sharing: computational evaluations of docking methods
Bias, reporting, and sharing: computational evaluations of docking methods
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DOI：
10.1007/s10822-007-9151-x
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发表时间：
2008-03-01
影响因子：
3.5
通讯作者：
Jain, Ajay N.
中科院分区：
文献类型：
--
作者：
Jain, Ajay N.
将配体对接到蛋白质结合位点的计算方法在药物发现中已经变得普遍存在。尽管该领域的年龄，没有标准已经建立对接准确性，虚拟筛选效用，或评分准确性的方法学评价。在数据共享、数据集设计和编制以及统计报告方面存在一些关键问题，这些问题影响到报告转化为实际业绩的程度。这些问题也影响到方法的改变与报告的业绩改进之间是否存在透明的关系。本文件详细列举了每个领域的缺陷，并就最佳做法提出了建议。
Computational methods for docking ligands to protein binding sites have become ubiquitous in drug discovery. Despite the age of the field, no standards have been established with respect to methodological evaluation of docking accuracy, virtual screening utility, or scoring accuracy. There are critical issues relating to data sharing, data set design and preparation, and statistical reporting that have an impact on the degree to which a report will translate into real-world performance. These issues also have an impact on whether there is a transparent relationship between methodological changes and reported performance improvements. This paper presents detailed examples of pitfalls in each area and makes recommendations as to best practices.