State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Xueyuan Road 38, Haidian District, 100191, Beijing, P.R. China.
Academy for Advanced Interdisciplinary Studies, Peking University, Yiheyuan Road 5, Haidian District, Beijing, P.R. China.
Mol Inform. 2022 Apr;41(4):e2100063. doi: 10.1002/minf.202100063. Epub 2021 Nov 17.
As an efficient way of computational target prediction, reverse docking can find not only potential targets but also binding modes for a query ligand. Though the number of available docking tools keeps expanding, there is still not a comprehensive evaluation study which can uncover the advantages and limitations of these strategies in the research field of computational target-fishing. In this study, we propose a brand-new evaluation dataset tailor-made for reverse docking, which is composed of a true positive set (the core set) and two negative sets (the similar decoy set and the dissimilar decoy set). The proposed evaluation dataset can assess the prediction performance of docking tools as various values affected by varying degrees of inter-target ranking bias. The performance of four classical docking programs (AutoDock, AutoDock Vina, Glide and GOLD) was evaluated utilizing our dataset, and a biased prediction performance was observed regarding binding site properties. The results demonstrated that Glide (SP) and Glide(XP) had the best capacity to find true targets whether there was inter-target ranking bias or not.
作为一种有效的计算靶标预测方法，反向对接不仅可以找到潜在的靶标，还可以找到查询配体的结合模式。尽管可用的对接工具数量不断增加，但在计算靶标搜索研究领域，仍然没有一个全面的评估研究可以揭示这些策略的优缺点。在这项研究中，我们提出了一个全新的、专门针对反向对接的评估数据集，该数据集由一个真实阳性集（核心集）和两个阴性集（相似诱饵集和不相似诱饵集）组成。该评估数据集可以评估对接工具的预测性能，因为各种值会受到不同程度的靶标间排序偏差的影响。利用我们的数据集评估了四个经典的对接程序（AutoDock、AutoDock Vina、Glide 和 GOLD）的性能，发现对接程序在结合位点性质方面存在有偏差的预测性能。结果表明，无论是否存在靶标间排序偏差，Glide（SP）和 Glide（XP）都有最好的能力找到真实的靶标。
