Modeling polypharmacy side effects with graph convolutional networks
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Abstract
Motivation: The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases or co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Polypharmacy side effects emerge because of drug–drug interactions, in which activity of one drug may change, favorably or unfavorably, if taken with another drug. The knowledge of drug interactions is often limited because these complex relationships are rare, and are usually not observed in relatively small clinical testing. Discovering polypharmacy side effects thus remains an important challenge with significant implications for patient mortality and morbidity. Results: Here, we present Decagon, an approach for modeling polypharmacy side effects. The approach constructs a multimodal graph of protein–protein interactions, drug–protein target interactions and the polypharmacy side effects, which are represented as drug–drug interactions, where each side effect is an edge of a different type. Decagon is developed specifically to handle such multimodal graphs with a large number of edge types. Our approach develops a new graph convolutional neural network for multirelational link prediction in multimodal networks. Unlike approaches limited to predicting simple drug–drug interaction values, Decagon can predict the exact side effect, if any, through which a given drug combination manifests clinically. Decagon accurately predicts polypharmacy side effects, outperforming baselines by up to 69%. We find that it automatically learns representations of side effects indicative of co-occurrence of polypharmacy in patients. Furthermore, Decagon models particularly well polypharmacy side effects that have a strong molecular basis, while on predominantly non-molecular side effects, it achieves good performance because of effective sharing of model parameters across edge types. Decagon opens up opportunities to use large pharmacogenomic and patient population data to flag and prioritize polypharmacy side effects for follow-up analysis via formal pharmacological studies. Availability and implementation: Source code and preprocessed datasets are at: http://snap.stanford.edu/decagon.
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GCNNs have been used to model drug combinations, drug-target interactions, and multiple property predictions.
“GCNNs have been used to model drug combinations (90), drug-target interactions (91), and multiple property predictions (92).”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [91]
Geometric deep-learning models optimized on large medical KGs can match disease signatures to therapeutic candidates based on networks perturbed in disease.
“To provide therapeutic predictions, geometric deep-learning models optimized on large medical KGs15 can match disease signatures to therapeutic candidates based on networks perturbed in disease15–18.”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [18]
The standard benchmarking strategy used to evaluate drug-repurposing AI models aligns with the approach predominantly used in the literature.
“This evaluation method aligns with the approach predominantly used in the literature12,14–16,18–20.”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [18]
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Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones
Bai, Y., Ying, R., Ren, H., & Leskovec, J. (2021) · arXiv · doi:10.48550/arxiv.2110.14923 · Cited by 31 · see citing works
“Knowledge graphs play an important role in many scientific and machine learning applications, including question answering [2], information retrieval [3] and discovery in biomedicine [4].”
BackgroundSelf-citationFormatting biological big data for modern machine learning in drug discovery
Duran‐Frigola, M., Fernández‐Torras, A., Bertoni, M., & Aloy, P. (2019) · WIREs Computational Molecular Science · doi:10.1002/wcms.1408 · Cited by 25 · see citing works
Graph convolutional networks for epigenetic state prediction using both sequence and 3D genome data
Lanchantin, J., & Qi, Y. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa793 · Cited by 17 · see citing works
Fan, K., & Zhang, Y. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00807 · Cited by 7 · see citing works
Xie, Y., Peng, J., & Zhou, Y. (2019) · doi:10.21203/rs.2.18936/v1 · Cited by 5 · see citing works
- Authors
- Zitnik, M., Agrawal, M., & Leskovec, J.
- Affiliations
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Stanford University (US)
- Chan Zuckerberg Biohub San Francisco (US)
- Corresponding author
- Leskovec, Jure
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-06-27
- Volume
- 34
- Issue
- 13
- Pages
- i457-i466
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC6022705
- OpenAlex
- W2786016794
- Cited by
- 1,501
- References
- 70
- Citations by year
- 2026: 255 · 2025: 229 · 2024: 165 · 2023: 196 · 2022: 192 · 2021: 217 · 2020: 162 · 2019: 71 · 2018: 13
- FWCI
- 86.95
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- NSF (IIS-1149837)
- NIH (BD2K U54EB020405)
- DARPA SIMPLEX
- Stanford Data Science Initiative
- Chan Zuckerberg Biohub
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- polypharmacy, drug-drug interactions, adverse drug reactions, drug side effect prediction, multimodal graphs, graph convolutional networks, protein-protein interaction, decagon
- MeSH terms
- Data Visualization, Drug Interactions, Female, Humans, Models, Biological, Software, Neural Networks, Computer, Computational Biology, Polypharmacy, Protein Interaction Maps, Drug-Related Side Effects and Adverse Reactions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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