Structure-based protein–ligand interaction fingerprints for binding affinity prediction

Binding affinity prediction (BAP) using protein–ligand complex structures is crucial to computer-aided drug design, but remains a challenging problem. To achieve efficient and accurate BAP, machine-learning scoring functions (SFs) based on a wide range of descriptors have been developed. Among those...

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Autores principales:	Debby D. Wang, Moon-Tong Chan, Hong Yan
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Interaction fingerprint Protein–ligand binding affinity Scoring function Machine learning Computer-aided drug design Biotechnology TP248.13-248.65
Acceso en línea:	https://doaj.org/article/0eb163c916dc4917b73286811f8c9bd6
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spelling	oai:doaj.org-article:0eb163c916dc4917b73286811f8c9bd62021-11-30T04:15:25ZStructure-based protein–ligand interaction fingerprints for binding affinity prediction2001-037010.1016/j.csbj.2021.11.018https://doaj.org/article/0eb163c916dc4917b73286811f8c9bd62021-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2001037021004839https://doaj.org/toc/2001-0370Binding affinity prediction (BAP) using protein–ligand complex structures is crucial to computer-aided drug design, but remains a challenging problem. To achieve efficient and accurate BAP, machine-learning scoring functions (SFs) based on a wide range of descriptors have been developed. Among those descriptors, protein–ligand interaction fingerprints (IFPs) are competitive due to their simple representations, elaborate profiles of key interactions and easy collaborations with machine-learning algorithms. In this paper, we have adopted a building-block-based taxonomy to review a broad range of IFP models, and compared representative IFP-based SFs in target-specific and generic scoring tasks. Atom-pair-counts-based and substructure-based IFPs show great potential in these tasks.Debby D. WangMoon-Tong ChanHong YanElsevierarticleInteraction fingerprintProtein–ligand binding affinityScoring functionMachine learningComputer-aided drug designBiotechnologyTP248.13-248.65ENComputational and Structural Biotechnology Journal, Vol 19, Iss , Pp 6291-6300 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Interaction fingerprint Protein–ligand binding affinity Scoring function Machine learning Computer-aided drug design Biotechnology TP248.13-248.65
spellingShingle	Interaction fingerprint Protein–ligand binding affinity Scoring function Machine learning Computer-aided drug design Biotechnology TP248.13-248.65 Debby D. Wang Moon-Tong Chan Hong Yan Structure-based protein–ligand interaction fingerprints for binding affinity prediction
description	Binding affinity prediction (BAP) using protein–ligand complex structures is crucial to computer-aided drug design, but remains a challenging problem. To achieve efficient and accurate BAP, machine-learning scoring functions (SFs) based on a wide range of descriptors have been developed. Among those descriptors, protein–ligand interaction fingerprints (IFPs) are competitive due to their simple representations, elaborate profiles of key interactions and easy collaborations with machine-learning algorithms. In this paper, we have adopted a building-block-based taxonomy to review a broad range of IFP models, and compared representative IFP-based SFs in target-specific and generic scoring tasks. Atom-pair-counts-based and substructure-based IFPs show great potential in these tasks.
format	article
author	Debby D. Wang Moon-Tong Chan Hong Yan
author_facet	Debby D. Wang Moon-Tong Chan Hong Yan
author_sort	Debby D. Wang
title	Structure-based protein–ligand interaction fingerprints for binding affinity prediction
title_short	Structure-based protein–ligand interaction fingerprints for binding affinity prediction
title_full	Structure-based protein–ligand interaction fingerprints for binding affinity prediction
title_fullStr	Structure-based protein–ligand interaction fingerprints for binding affinity prediction
title_full_unstemmed	Structure-based protein–ligand interaction fingerprints for binding affinity prediction
title_sort	structure-based protein–ligand interaction fingerprints for binding affinity prediction
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/0eb163c916dc4917b73286811f8c9bd6
work_keys_str_mv	AT debbydwang structurebasedproteinligandinteractionfingerprintsforbindingaffinityprediction AT moontongchan structurebasedproteinligandinteractionfingerprintsforbindingaffinityprediction AT hongyan structurebasedproteinligandinteractionfingerprintsforbindingaffinityprediction
_version_	1718406785971257344

Structure-based protein–ligand interaction fingerprints for binding affinity prediction

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