Journal Article

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

Choi, Jonghwan, Ahn, Jaegyoon, Park, Sanghyun, Seo, Sangmin

2021

Overview

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 .

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: 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