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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Bibliographic Details
Published in:BMC bioinformatics Vol. 22; no. 1; pp. 1 - 15
Main Authors: Seo, Sangmin, Choi, Jonghwan, Park, Sanghyun, Ahn, Jaegyoon
Format: Journal Article
Language:English
Published: London BioMed Central 08.11.2021
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
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 Access:Get full text
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Summary: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 tend to have limitations, mainly resulting from a lack of sufficient energy terms to describe the complex interactions between proteins and ligands. Recent deep-learning techniques can potentially solve this problem. However, the search for more efficient and appropriate deep-learning architectures and methods to represent protein–ligand complex is ongoing. Results In this study, we proposed a deep-neural network model to improve the prediction accuracy of protein–ligand complex binding affinity. The proposed model has two important features, descriptor embeddings with information on the local structures of a protein–ligand complex and an attention mechanism to highlight important descriptors for binding affinity prediction. The proposed model performed better than existing binding affinity prediction models on most benchmark datasets. Conclusions We confirmed that an attention mechanism can capture the binding sites in a protein–ligand complex to improve prediction performance. Our code is available at https://github.com/Blue1993/BAPA .
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-021-04466-0