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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8637032/ https://www.ncbi.nlm.nih.gov/pubmed/34900139 http://dx.doi.org/10.1016/j.csbj.2021.11.018 |
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author | Wang, Debby D. Chan, Moon-Tong Yan, Hong |
author_facet | Wang, Debby D. Chan, Moon-Tong Yan, Hong |
author_sort | Wang, Debby D. |
collection | PubMed |
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 | Online Article Text |
id | pubmed-8637032 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
spelling | pubmed-86370322021-12-09 Structure-based protein–ligand interaction fingerprints for binding affinity prediction Wang, Debby D. Chan, Moon-Tong Yan, Hong Comput Struct Biotechnol J Review 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. Research Network of Computational and Structural Biotechnology 2021-11-25 /pmc/articles/PMC8637032/ /pubmed/34900139 http://dx.doi.org/10.1016/j.csbj.2021.11.018 Text en © 2021 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Review Wang, Debby D. Chan, Moon-Tong Yan, Hong Structure-based protein–ligand interaction fingerprints for binding affinity prediction |
title | 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_short | Structure-based protein–ligand interaction fingerprints for binding affinity prediction |
title_sort | structure-based protein–ligand interaction fingerprints for binding affinity prediction |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8637032/ https://www.ncbi.nlm.nih.gov/pubmed/34900139 http://dx.doi.org/10.1016/j.csbj.2021.11.018 |
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