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

[Display omitted] 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 deve...

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Bibliographic Details
Published in:Computational and structural biotechnology journal 2021-01, Vol.19, p.6291-6300
Main Authors: Wang, Debby D., Chan, Moon-Tong, Yan, Hong
Format: Article
Language:English
Subjects:
Computer-aided drug design
Interaction fingerprint
Machine learning
Protein–ligand binding affinity
Review
Scoring function
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Summary:[Display omitted] 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.
ISSN:2001-0370
2001-0370
DOI:10.1016/j.csbj.2021.11.018