ArticleNature communications2021
Identification of disease treatment mechanisms through the multiscale interactome.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers.
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Who cites it
100 citing papers in PubMed.
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- CAREPath: semantic context-aware reasoning paths with mechanism-augmented embeddings for drug repurposing.Briefings in bioinformatics · 2026Article
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- PregMedNet: Multifaceted maternal medication impacts on neonatal complications.Nature communications · 2026Article
- Integration of genetic evidence to identify approved drug targets.Genome medicine · 2026Article
- Integrating Divergence-Based Proteomic Analysis and Directed Network Diffusion to Characterize Diagnosis-Anchored Molecular Variability at the Metabolic Syndrome-Migraine Interface.International journal of molecular sciences · 2026Article
- Mechanistic Insights into the Role of Artificial Intelligence and Machine Learning in the Diagnosis and Management of Multiple Sclerosis.Pathophysiology : the official journal of the International Society for Pathophysiology · 2026Review
- Tomentosin selectively targets microglial pyroptosis to overcome fluoxetine-resistant depression: a network-based therapeutic discovery.Translational psychiatry · 2026Article
- Combinatorial prediction of therapeutic perturbations using causally inspired neural networks.Nature biomedical engineering · 2026Article
- A network-based map of the chemical exposome connects molecular interactions to public health.Nature communications · 2026Article
- Explainable drug side effect prediction in central neural system via biologically informed graph neural network.Translational psychiatry · 2026Article
- Multiscale Interactome-Guided Prioritization of Candidate Herbs and Active Compounds for Hepatic Cirrhosis Using a Biased Random Walk Algorithm.Current issues in molecular biology · 2026Article
- Genomics of drug target prioritization for complex diseases.Nature reviews. Genetics · 2026Review
- BioMNEDR: mechanism-guided network embedding for drug repurposing.Briefings in bioinformatics · 2026Article
- Curated and Structure-Based Drug-Target Interactions Improve Underprediction of Drug Side Effects in Network Models.Journal of chemical information and modeling · 2026Article
- Using Biological Networks to Guide Biomedical Prediction.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Multilayer network approaches to omics data integration in digital twins for cancer research.Frontiers in systems biology · 2026Review
40 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Most diseases disrupt multiple proteins, and drugs treat such diseases by restoring the functions of the disrupted proteins. How drugs restore these functions, however, is often unknown as a drug's therapeutic effects are not limited to the proteins that the drug directly targets. Here, we develop the multiscale interactome, a powerful approach to explain disease treatment. We integrate disease-perturbed proteins, drug targets, and biological functions into a multiscale interactome network. We then develop a random walk-based method that captures how drug effects propagate through a hierarchy of biological functions and physical protein-protein interactions. On three key pharmacological tasks, the multiscale interactome predicts drug-disease treatment, identifies proteins and biological functions related to treatment, and predicts genes that alter a treatment's efficacy and adverse reactions. Our results indicate that physical interactions between proteins alone cannot explain treatment since many drugs treat diseases by affecting the biological functions disrupted by the disease rather than directly targeting disease proteins or their regulators. We provide a general framework for explaining treatment, even when drugs seem unrelated to the diseases they are recommended for.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.