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

Autor:	Orhobor, Oghenejokpeme I, Rehim, Abbi Abdel, Lou, Hang, Ni, Hao, King, Ross D
Přispěvatelé:	Orhobor, Oghenejokpeme I. [0000-0003-1178-611X], King, Ross D. [0000-0001-7208-4387], Apollo - University of Cambridge Repository, Orhobor, Oghenejokpeme I [0000-0003-1178-611X], King, Ross D [0000-0001-7208-4387], King, Ross [0000-0001-7208-4387]
Rok vydání:	2022
Předmět:	scoring functions protein binding affinity prediction Multidisciplinary machine learning Biochemistry cellular and molecular biology Research articles
DOI:	10.17863/cam.84264
Popis:	Peer reviewed: True 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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::65d19a906389e8ad67c84e1f3d990665 Zobrazit plný text záznamu