A simple spatial extension to the extended connectivity interaction features for binding affinity prediction
Authors
Orhobor, Oghenejokpeme I
Rehim, Abbi Abdel
Lou, Hang
Ni, Hao
King, Ross D
Publication Date
2022-05-04Journal Title
Royal Society Open Science
ISSN
2054-5703
Publisher
The Royal Society
Volume
9
Issue
5
Language
en
Type
Article
This Version
AO
VoR
Metadata
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Orhobor, O. I., Rehim, A. A., Lou, H., Ni, H., & King, R. D. (2022). A simple spatial extension to the extended connectivity interaction features for binding affinity prediction. Royal Society Open Science, 9 (5) https://doi.org/10.1098/rsos.211745
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.
Keywords
Biochemistry, cellular and molecular biology, Research articles, machine learning, protein binding affinity prediction, scoring functions
Sponsorship
Engineering and Physical Sciences Research Council (EP/N510129/1, EP/R022925/1, EP/R022941/1, EP/S026347/1)
Identifiers
rsos211745
External DOI: https://doi.org/10.1098/rsos.211745
This record's URL: https://www.repository.cam.ac.uk/handle/1810/336845
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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