Binding affinity prediction for protein–ligand complex using deep attention mechanism based on intermolecular interactions

Background Accurate prediction of protein–ligand binding affinity is important for lowering the overall cost of drug discovery in structure-based drug design. For accurate predictions, many classical scoring functions and machine learning-based methods have been developed. However, these techniques...

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Veröffentlicht in:	BMC bioinformatics Jg. 22; H. 1; S. 1 - 15
Hauptverfasser:	Seo, Sangmin, Choi, Jonghwan, Park, Sanghyun, Ahn, Jaegyoon
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 08.11.2021 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Accuracy Affinity Algorithms Analysis Attention mechanism Binding affinity Binding sites Bioinformatics Biomedical and Life Sciences Computational Biology/Bioinformatics Computer Appl. in Life Sciences Coordination compounds Datasets Deep learning Drug development Drug discovery Drugs Learning algorithms Life Sciences Ligands Machine learning Methods Microarrays Neural networks Performance evaluation Prediction models Protein binding Proteins Protein–ligand complex Structure-activity relationship (Pharmacology) Structure-activity relationships Structure-based drug design South Korea Attention mechanism Protein–ligand complex Structure-based drug design Binding affinity
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
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