Orhobor, Oghenejokpeme I;
Rehim, Abbi Abdel;
Lou, Hang;
Ni, Hao;
King, Ross D;
(2022)
A simple spatial extension to the extended connectivity interaction features for binding affinity prediction.
Royal Society Open Science
, 9
(5)
, Article 211745. 10.1098/rsos.211745.
Preview |
Text
Ni_rsos.211745.pdf Download (384kB) | Preview |
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.
| Type: | Article |
|---|---|
| Title: | A simple spatial extension to the extended connectivity interaction features for binding affinity prediction |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1098/rsos.211745 |
| Publisher version: | https://doi.org/10.1098/rsos.211745 |
| Language: | English |
| Additional information: | © 2022 The Authors. Published by the Royal Society under the terms of the CreativeCommons Attribution License http://creativecommons.org/licenses/by/4.0/, which permitsunrestricted use, provided the original author and source are credited. |
| Keywords: | machine learning, protein binding affinity prediction, scoring functions |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Mathematics UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10149446 |
Archive Staff Only
 |
View Item |
