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Interpretable bilinear attention network with domain adaptation improves drug–target prediction

Nature Machine Intelligence volume 5, pages 126–136 (2023)Cite this article

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A preprint version of the article is available at arXiv.

Abstract

Predicting drug–target interaction is key for drug discovery. Recent deep learning-based methods show promising performance, but two challenges remain: how to explicitly model and learn local interactions between drugs and targets for better prediction and interpretation and how to optimize generalization performance of predictions on novel drug–target pairs. Here, we present DrugBAN, a deep bilinear attention network (BAN) framework with domain adaptation to explicitly learn pairwise local interactions between drugs and targets, and adapt in response to out-of-distribution data. DrugBAN works on drug molecular graphs and target protein sequences to perform prediction, with conditional domain adversarial learning to align learned interaction representations across different distributions for better generalization on novel drug–target pairs. Experiments on three benchmark datasets under both in-domain and cross-domain settings show that DrugBAN achieves the best overall performance against five state-of-the-art baseline models. Moreover, visualizing the learned bilinear attention map provides interpretable insights from prediction results.

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Fig. 1: Overview of the DrugBAN framework.
Fig. 2: In-domain performance comparison on the Human dataset with random split and cold pair split (statistics over five random runs).
Fig. 3: Cross-domain performance comparison on the BindingDB and BioSNAP datasets with clustering-based pair split (statistics over five random runs).
Fig. 4: Visualization of ligands and binding pockets for interpretability study.

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Data availability

The experimental data used in this work are available at https://github.com/peizhenbai/DrugBAN/tree/main/datasets. All data used in this work are from public resources. The BindingDB48 source can be found at https://www.bindingdb.org/bind/index.jsp; the BioSNAP17,30 source can be found at https://github.com/kexinhuang12345/MolTrans/tree/master/dataset/BIOSNAP/full_data and the Human31 source used in a previous study16 can be found at https://github.com/lifanchen-simm/transformerCPI/blob/master/Human%2CC.elegans/dataset/human_data.txt. The co-crystalized ligands from PDB40 are available at https://www.rcsb.org by searching their PDB IDs.

Code availability

The source code and implementation details of DrugBAN are freely available at both GitHub repository (https://github.com/peizhenbai/DrugBAN) and CodeOcean capsule (https://doi.org/10.24433/CO.3558316.v1)57. The code is also archived at Zenodo (https://doi.org/10.5281/zenodo.7231657)58.

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Acknowledgements

We thank S. Zhou, X. Liu and L. Schöbs for their helpful suggestions and discussion on the work. P.B. is supported by a University of Sheffield Faculty of Engineering Research Scholarship (grant no. 169426530).

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Authors and Affiliations

  1. Department of Computer Science, University of Sheffield, Sheffield, UK

    Peizhen Bai & Haiping Lu

  2. Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden

    Filip Miljković

  3. Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Waltham, USA

    Bino John

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Contributions

P.B., F.M., B.J. and H.L. conceived and designed the work. P.B. developed the models and performed the experiments under the guidance of B.J. and H.L. F.M. and P.B. analysed the data and conducted method comparisons. F.M. contributed to materials and the analysis tool. All authors contributed to writing the paper.

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Correspondence to Haiping Lu.

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Bai, P., Miljković, F., John, B. et al. Interpretable bilinear attention network with domain adaptation improves drug–target prediction. Nat Mach Intell 5, 126–136 (2023). https://doi.org/10.1038/s42256-022-00605-1

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