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Cross‐docking benchmark for automated pose and ranking prediction of ligand binding
Author(s) -
Wierbowski Shayne D.,
Wingert Bentley M.,
Zheng Jim,
Camacho Carlos J.
Publication year - 2020
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1002/pro.3784
Subject(s) - docking (animal) , computer science , protein–ligand docking , benchmarking , benchmark (surveying) , data mining , artificial intelligence , virtual screening , machine learning , drug discovery , bioinformatics , biology , medicine , nursing , geography , business , geodesy , marketing
Significant efforts have been devoted in the last decade to improving molecular docking techniques to predict both accurate binding poses and ranking affinities. Some shortcomings in the field are the limited number of standard methods for measuring docking success and the availability of widely accepted standard data sets for use as benchmarks in comparing different docking algorithms throughout the field. In order to address these issues, we have created a Cross-Docking Benchmark server. The server is a versatile cross-docking data set containing 4,399 protein-ligand complexes across 95 protein targets intended to serve as benchmark set and gold standard for state-of-the-art pose and ranking prediction in easy, medium, hard, or very hard docking targets. The benchmark along with a customizable cross-docking data set generation tool is available at http://disco.csb.pitt.edu. We further demonstrate the potential uses of the server in questions outside of basic benchmarking such as the selection of the ideal docking reference structure.