Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7389
Title: Machine-learning scoring functions trained on complexes dissimilar to the test set already outperform classical counterparts on a blind benchmark
Authors: Li, Hongjian 
Lu, gang 
Sze, Kam-Heung 
Su, Xianwei 
Chan, Wai-Yee 
Prof. LEUNG Kwong Sak 
Issue Date: 2021
Source: Briefings in Bioinformatics, November 2021, Vol. 22 (6), bbab225
Journal: Briefings in Bioinformatics 
Abstract: The superior performance of machine-learning scoring functions for docking has caused a series of debates on whether it is due to learning knowledge from training data that are similar in some sense to the test data. With a systematically revised methodology and a blind benchmark realistically mimicking the process of prospective prediction of binding affinity, we have evaluated three broadly used classical scoring functions and five machine-learning counterparts calibrated with both random forest and extreme gradient boosting using both solo and hybrid features, showing for the first time that machine-learning scoring functions trained exclusively on a proportion of as low as 8% complexes dissimilar to the test set already outperform classical scoring functions, a percentage that is far lower than what has been recently reported on all the three CASF benchmarks. The performance of machine-learning scoring functions is underestimated due to the absence of similar samples in some artificially created training sets that discard the full spectrum of complexes to be found in a prospective environment. Given the inevitability of any degree of similarity contained in a large dataset, the criteria for scoring function selection depend on which one can make the best use of all available materials. Software code and data are provided at https://github.com/cusdulab/MLSF for interested readers to rapidly rebuild the scoring functions and reproduce our results, even to make extended analyses on their own benchmarks.
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/7389
ISSN: 1477-4054
DOI: 10.1093/bib/bbab225
Appears in Collections:Applied Data Science - Publication

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