Machine-learning scoring functions trained on complexes dissimilar to the test set already outperform classical counterparts on a blind benchmark.

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 l

SUBMITTER: Li H 

PROVIDER: S-EPMC8575004 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature