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

• Main Authors: Ahn, J. (Author), Choi, J. (Author), Park, S. (Author), Seo, S. (Author)
• Format: Article
• Language: English
• Published: BioMed Central Ltd 2021
• Subjects: Accurate prediction; Attention mechanism; Attention mechanisms; Binding affinities; Binding affinity; Binding energy; binding site; Binding sites; Binding Sites; Complexation; Deep neural networks; Drug discovery; Forecasting; Intermolecular interactions; ligand; Ligands; machine learning; Machine Learning; Mechanism-based; metabolism; Overall costs; protein; protein binding; Protein Binding; Protein-ligand binding affinities; Protein–ligand complex; Protein-ligand complexes; Proteins; Structure based drug designs; Structure-based drug design
• Online Access: View Fulltext in Publisher

 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. © 2021, The Author(s).