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

Sangmin Seo, Jonghwan Choi, Sanghyun Park, Jaegyoon Ahn

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


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

Original languageEnglish
Article number542
JournalBMC bioinformatics
Issue number1
Publication statusPublished - 2021 Dec

Bibliographical note

Funding Information:
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT [NRF-2019R1A2C3005212]. The funders did not play any role in the design of the study, data collection, analysis, or preparation of the manuscript.

Publisher Copyright:
© 2021, The Author(s).

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics


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