Comparing sixteen scoring functions for predicting biological activities of ligands for protein targets

Xu, Weijun, Lucke, Andrew J. and Fairlie, David P. (2015) Comparing sixteen scoring functions for predicting biological activities of ligands for protein targets. Journal of Molecular Graphics and Modelling, 57 76-88. doi:10.1016/j.jmgm.2015.01.009

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Author Xu, Weijun
Lucke, Andrew J.
Fairlie, David P.
Title Comparing sixteen scoring functions for predicting biological activities of ligands for protein targets
Journal name Journal of Molecular Graphics and Modelling   Check publisher's open access policy
ISSN 1873-4243
Publication date 2015-04-01
Year available 2015
Sub-type Article (original research)
DOI 10.1016/j.jmgm.2015.01.009
Open Access Status Not yet assessed
Volume 57
Start page 76
End page 88
Total pages 13
Place of publication Philadelphia PA, United States
Publisher Elsevier
Language eng
Abstract Accurately predicting relative binding affinities and biological potencies for ligands that interact with proteins remains a significant challenge for computational chemists. Most evaluations of docking and scoring algorithms have focused on enhancing ligand affinity for a protein by optimizing docking poses and enrichment factors during virtual screening. However, there is still relatively limited information on the accuracy of commercially available docking and scoring software programs for correctly predicting binding affinities and biological activities of structurally related inhibitors of different enzyme classes. Presented here is a comparative evaluation of eight molecular docking programs (Autodock Vina, Fitted, FlexX, Fred, Glide, GOLD, LibDock, MolDock) using sixteen docking and scoring functions to predict the rank-order activity of different ligand series for six pharmacologically important protein and enzyme targets (Factor Xa, Cdk2 kinase, Aurora A kinase, COX-2, pla2g2a, β Estrogen receptor). Use of Fitted gave an excellent correlation (Pearson 0.86, Spearman 0.91) between predicted and experimental binding only for Cdk2 kinase inhibitors. FlexX and GOLDScore produced good correlations (Pearson > 0.6) for hydrophilic targets such as Factor Xa, Cdk2 kinase and Aurora A kinase. By contrast, pla2g2a and COX-2 emerged as difficult targets for scoring functions to predict ligand activities. Although possessing a high hydrophobicity in its binding site, β Estrogen receptor produced reasonable correlations using LibDock (Pearson 0.75, Spearman 0.68). These findings can assist medicinal chemists to better match scoring functions with ligand-target systems for hit-to-lead optimization using computer-aided drug design approaches.
Keyword Molecular docking
Scoring functions
Hydrophilic vs hydrophobic targets
Drug design
Enzyme inhibitor
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2016 Collection
Institute for Molecular Bioscience - Publications
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Citation counts: TR Web of Science Citation Count  Cited 16 times in Thomson Reuters Web of Science Article | Citations
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