Network-based in silico drug efficacy screening
Open accessAbstract
The increasing cost of drug development together with a significant drop in the number of new drug approvals raises the need for innovative approaches for target identification and efficacy prediction. Here, we take advantage of our increasing understanding of the network-based origins of diseases to introduce a drug-disease proximity measure that quantifies the interplay between drugs targets and diseases. By correcting for the known biases of the interactome, proximity helps us uncover the therapeutic effect of drugs, as well as to distinguish palliative from effective treatments. Our analysis of 238 drugs used in 78 diseases indicates that the therapeutic effect of drugs is localized in a small network neighborhood of the disease genes and highlights efficacy issues for drugs used in Parkinson and several inflammatory disorders. Finally, network-based proximity allows us to predict novel drug-disease associations that offer unprecedented opportunities for drug repurposing and the detection of adverse effects.
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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 [19]
Because gene expression data can be noisy and variable, recent work fuses co-expression matrices with existing biomedical networks such as GO annotations and PPI, then feeds the resulting graph into graph convolution layers.
“Because gene expression data can be noisy and variational, recent advances include fusing the co-expression matrices with existing biomedical networks, such as GO annotations and PPI, and feeding the resulting graph into graph convolution layers (Section 2.2) [150, 151, 152].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [151]
This fusion has enabled more interpretable disease classification models, for example by weighting gene interactions using existing biological knowledge.
“Doing so has enabled more interpretable disease classification models (e.g., weighting gene interactions based on existing biological knowledge).”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [151]
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- Authors
- Guney, E., Menche, J., Vidal, M., & Barábasi, A.-L.
- Affiliations
- Northeastern University (US)
- Harvard University (US)
- Venue
- Nature Communications
- Published
- 2016-02-01
- Volume
- 7
- Issue
- 1
- Pages
- 10331-10331
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC4740350
- OpenAlex
- W2263739890
- Cited by
- 625
- References
- 52
- Citations by year
- 2026: 82 · 2025: 74 · 2024: 63 · 2023: 57 · 2022: 67 · 2021: 75 · 2020: 64 · 2019: 43 · 2018: 51 · 2017: 39 · 2016: 10
- FWCI
- 24.78
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Open Research (Auckland University of Technology) (submittedVersion, cc-by)
- Funders
- National Institutes of Health
- National Heart, Lung, and Blood Institute (U01HL108630)
- National Human Genome Research Institute (50-HG004233, P50HG004233)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- drug efficacy prediction, protein-protein interaction network, drug-target network, adverse drug reactions, Parkinson's disease, inflammatory diseases, disease-associated genes
- MeSH terms
- Computer Simulation, Models, Biological, Structure-Activity Relationship, Molecular Structure, Neural Networks, Computer, Drug Discovery
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