Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders
Open accessAbstract
Drug similarity has been studied to support downstream clinical tasks such as inferring novel properties of drugs (e.g. side effects, indications, interactions) from known properties. The growing availability of new types of drug features brings the opportunity of learning a more comprehensive and accurate drug similarity that represents the full spectrum of underlying drug relations. However, it is challenging to integrate these heterogeneous, noisy, nonlinear-related information to learn accurate similarity measures especially when labels are scarce. Moreover, there is a trade-off between accuracy and interpretability. In this paper, we propose to learn accurate and interpretable similarity measures from multiple types of drug features. In particular, we model the integration using multi-view graph auto-encoders, and add attentive mechanism to determine the weights for each view with respect to corresponding tasks and features for better interpretability. Our model has flexible design for both semi-supervised and unsupervised settings. Experimental results demonstrated significant predictive accuracy improvement. Case studies also showed better model capacity (e.g. embed node features) and interpretability.
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Variational graph autoencoders can produce cell embeddings and attention weights indicating which genes the model uses for a given cell.
“Alternatively, variational graph autoencoders produce cell embeddings and interpretable attention weights indicating what genes the model attends to when deriving an embedding for a given cell [159].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [159]
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- Authors
- Ma, T., Xiao, C., Zhou, J., & Wang, F.
- Affiliations
- IBM (United States) (US)
- Michigan State University (US)
- Cornell University (US)
- Venue
- Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
- Published
- 2018-07
- Pages
- 3477-3483
- OpenAlex
- W2962876161
- Cited by
- 220
- References
- 26
- Citations by year
- 2026: 12 · 2025: 26 · 2024: 23 · 2023: 29 · 2022: 26 · 2021: 55 · 2020: 24 · 2019: 19 · 2018: 6
- FWCI
- 28.78
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1615597, 1750326, 1716432, 1749940)
- Office of Naval Research (N00014-17-1, N00014-17-1-2265, N00014, N00014-17)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- drug similarity, attention mechanism, interpretability, semi-supervised learning, unsupervised learning, drug property prediction, node embeddings
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