Abstract
Conditional domain adversarial learning presents a promising approach for enhancing the generalizability of deep learning-based methods. Inspired by the efficacy of conditional domain adversarial networks, Bai and colleagues introduced DrugBAN, a methodology designed to explicitly learn pairwise local interactions between drugs and targets. DrugBAN leverages drug molecular graphs and target protein sequences, employing conditional domain adversarial networks to improve the ability to adapt to out-of-distribution data and thereby ensuring superior prediction accuracy for new drug–target pairs. Here we examine the reusability of DrugBAN and extend the evaluation of its generalizability across a wider range of biomedical contexts beyond the original datasets. Various clustering-based strategies are implemented to resplit the source and target domains to assess the robustness of DrugBAN. We also apply this cross-domain adaptation technique to the prediction of cell line–drug responses and mutation–drug associations. The analysis serves as a stepping-off point to better understand and establish a general template applicable to link prediction tasks in biomedical bipartite networks.
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Data availability
The data used in our study are available via GitHub at https://github.com/vshy-dream/DrugBAN-reusability (ref. 22). All of the data used in the original paper by Bai et al. for testing DrugBAN are available via GitHub at https://github.com/peizhenbai/DrugBAN/tree/main/datasets (ref. 23).
Code availability
The original DrugBAN code is available via GitHub at https://github.com/peizhenbai/DrugBAN (ref. 23). Our modified DrugBAN version with changes is available via GitHub at https://github.com/vshy-dream/DrugBAN-reusability (ref. 22).
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (grant number 62102004) and the Introduction and Stabilization of Talent Project of Anhui Agricultural University (grant number yj2019-32).
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Z.Y. conceived and supervised the project. H.S., T.X. and Z.Y. designed the computational experiments and data analyses. H.S. and X.W. prepared the data. H.S., T.X. and W.G. implemented the methods, conducted the experiments and performed data analyses. H.S., T.X. and Z.Y. wrote the manuscript.
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Nature Machine Intelligence thanks Yi Xiong, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Xu, T., Shi, H., Gao, W. et al. Reusability report: Uncovering associations in biomedical bipartite networks via a bilinear attention network with domain adaptation. Nat Mach Intell 6, 461–466 (2024). https://doi.org/10.1038/s42256-024-00822-w
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DOI: https://doi.org/10.1038/s42256-024-00822-w
