DAPC Conference Proceedings
Graph neural networks for drug-drug interaction prediction in polypharmacy patients
We propose a heterogeneous graph neural network model for predicting adverse drug-drug interactions in patients taking multiple medications. The model encodes drug molecular structures, protein targets, and known side effects as a knowledge graph with 1.2 million edges. Evaluated on the DrugBank and TWOSIDES datasets, our approach achieves AUROC of 0.934 for predicting 963 distinct polypharmacy side effect types, outperforming existing methods by 4.7%.
Mishra Pooja, Park Jihoon, Osei Kwame et al.
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DAPC Conference Proceedings
Volume 3224 · ISSN: 1551-7616 · Published by DAPC Publishing
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About This Proceedings
DAPC Conference Proceedings Volume 3224 contains peer-reviewed research articles presented at DAPC 2026 — the 3rd International Conference on Data Analytics. The proceedings cover advances in Artificial Intelligence, Internet of Things, Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Healthcare Informatics, Cybersecurity, and related fields.
Published by DAPC and indexed in SCOPUS, these proceedings ensure global visibility, rigorous peer review, and lasting academic impact for all contributing researchers.
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DAPC Conference Proceedings
Volume / Issue
Vol. 3224, Issue 1
ISSN
1551-7616
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DAPC Publishing
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DAPC 2026
Conference Date
May 8-9, 2026
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