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A simple spatial extension to the extended connectivity interaction features for binding affinity prediction.

Published version
Peer-reviewed

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Authors

Orhobor, Oghenejokpeme I  ORCID logo  https://orcid.org/0000-0003-1178-611X
Rehim, Abbi Abdel 
Lou, Hang 
Ni, Hao 

Abstract

The representation of the protein-ligand complexes used in building machine learning models play an important role in the accuracy of binding affinity prediction. The Extended Connectivity Interaction Features (ECIF) is one such representation. We report that (i) including the discretized distances between protein-ligand atom pairs in the ECIF scheme improves predictive accuracy, and (ii) in an evaluation using gradient boosted trees, we found that the resampling method used in selecting the best hyperparameters has a strong effect on predictive performance, especially for benchmarking purposes.

Description

Peer reviewed: True

Keywords

machine learning, protein binding affinity prediction, scoring functions

Journal Title

R Soc Open Sci

Conference Name

Journal ISSN

2054-5703
2054-5703

Volume Title

Publisher

The Royal Society
Sponsorship
Engineering and Physical Sciences Research Council (EP/R022925/1)