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我们知道什么,以及我们何时知道?

What do we know and when do we know it?

作者信息

Nicholls Anthony

机构信息

OpenEye Scientific Software, Inc, 9D Bisbee Crt, Santa Fe, NM 87508, USA.

出版信息

J Comput Aided Mol Des. 2008 Mar-Apr;22(3-4):239-55. doi: 10.1007/s10822-008-9170-2. Epub 2008 Feb 6.

DOI:10.1007/s10822-008-9170-2
PMID:18253702
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2270923/
Abstract

Two essential aspects of virtual screening are considered: experimental design and performance metrics. In the design of any retrospective virtual screen, choices have to be made as to the purpose of the exercise. Is the goal to compare methods? Is the interest in a particular type of target or all targets? Are we simulating a 'real-world' setting, or teasing out distinguishing features of a method? What are the confidence limits for the results? What should be reported in a publication? In particular, what criteria should be used to decide between different performance metrics? Comparing the field of molecular modeling to other endeavors, such as medical statistics, criminology, or computer hardware evaluation indicates some clear directions. Taken together these suggest the modeling field has a long way to go to provide effective assessment of its approaches, either to itself or to a broader audience, but that there are no technical reasons why progress cannot be made.

摘要

虚拟筛选有两个重要方面需要考虑

实验设计和性能指标。在任何回顾性虚拟筛选的设计中,必须就该活动的目的做出选择。目的是比较方法吗?是对特定类型的靶点还是所有靶点感兴趣?我们是在模拟“现实世界”的情况,还是在梳理一种方法的显著特征?结果的置信限是多少?在出版物中应该报告什么?特别是,应该使用什么标准来决定不同的性能指标?将分子建模领域与其他领域进行比较,如医学统计学、犯罪学或计算机硬件评估,可得出一些明确的方向。综合来看,这些表明建模领域在向自身或更广泛的受众有效评估其方法方面还有很长的路要走,但没有技术上的原因表明无法取得进展。

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