Challenging Reverse Screening: A Benchmark Study for Comprehensive Evaluation. Issue 4 (17th November 2021)

• Record Type: Journal Article
• Authors: Li, Mingna; Hu, Jianxing; Wang, Yanxing; Li, Yibo; Zhang, Liangren; Liu, Zhenming
• Is Part Of: Molecular informatics. Volume 41:Issue 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-11-17
• Subjects: Target prediction -- Reverse docking -- Benchmark Dataset -- Evaluation; Cheminformatics -- Periodicals; QSAR (Biochemistry) -- Periodicals; Structure-activity relationships (Biochemistry) -- Periodicals; Drugs -- Structure-activity relationships -- Periodicals; 615.19
• DOI: 10.1002/minf.202100063
• Languages: English
• ISSNs: 1868-1743

Abstract

Abstract: 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. Abstract :