A simple spatial extension to the extended connectivity interaction features for binding affinity prediction
Journal article, 2022
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.
protein binding affinity prediction
machine learning
scoring functions
Author
Oghenejokpeme I. Orhobor
University of Cambridge
Abbi Abdel Rehim
University of Cambridge
Hang Lou
University of Cambridge
Hao Ni
Alan Turing Institute
University College London (UCL)
Ross King
University of Cambridge
Alan Turing Institute
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Royal Society Open Science
2054-5703 (eISSN)
Vol. 9 5 211745Subject Categories
Biochemistry and Molecular Biology
Biophysics
Bioinformatics (Computational Biology)
DOI
10.1098/rsos.211745