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

BMC Bioinformatics

Jaegyoon Ahn¹  Sanghyun Park²  Jonghwan Choi³  Sangmin Seo³ 

[1] Department of Computer Science and Engineering, Incheon National University, Incheon, Republic of Korea;Department of Computer Science, Yonsei University, Seoul, Republic of Korea;Department of Computer Science, Yonsei University, Seoul, Republic of Korea;UBLBio Corporation, 16679, Suwon, Republic of Korea;

关键词: Structure-based drug design;    Protein–ligand complex;    Binding affinity;    Attention mechanism;   

DOI  :  10.1186/s12859-021-04466-0

来源: Springer

【 摘 要 】

BackgroundAccurate 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.ResultsIn 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.ConclusionsWe 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.

【 授权许可】

CC BY