ArticleScientific reports2018
In silico profiling of systemic effects of drugs to predict unexpected interactions.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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Who cites it
11 citing papers in PubMed.
- Curated and Structure-Based Drug-Target Interactions Improve Underprediction of Drug Side Effects in Network Models.Journal of chemical information and modeling · 2026Article
- Across preclinical and clinical platforms, approved and investigational psychiatric drugs share pathways and associate with similar molecular functions.Frontiers in drug discovery · 2025Article
- Machine Learning Techniques for Predicting Drug-Related Side Effects: A Scoping Review.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Beta-2 adrenergic receptor agonism alters astrocyte phagocytic activity and has potential applications to psychiatric disease.Discover mental health · 2023Article
- A network paradigm predicts drug synergistic effects using downstream protein-protein interactions.CPT: pharmacometrics & systems pharmacology · 2022Article
- From random to predictive: a context-specific interaction framework improves selection of drug protein-protein interactions for unknown drug pathways.Integrative biology : quantitative biosciences from nano to macro · 2022Article
- Preclinical reserpine models recapitulating motor and non-motor features of Parkinson's disease: Roles of epigenetic upregulation of alpha-synuclein and autophagy impairment.Frontiers in pharmacology · 2022Article
- Molecular Docking: Shifting Paradigms in Drug Discovery.International journal of molecular sciences · 2019Review
- PathFX provides mechanistic insights into drug efficacy and safety for regulatory review and therapeutic development.PLoS computational biology · 2018Article
- Article
- Phenotype-oriented network analysis for discovering pharmacological effects of natural compounds.Scientific reports · 2018Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying unexpected drug interactions is an essential step in drug development. Most studies focus on predicting whether a drug pair interacts or is effective on a certain disease without considering the mechanism of action (MoA). Here, we introduce a novel method to infer effects and interactions of drug pairs with MoA based on the profiling of systemic effects of drugs. By investigating propagated drug effects from the molecular and phenotypic networks, we constructed profiles of 5,441 approved and investigational drugs for 3,833 phenotypes. Our analysis indicates that highly connected phenotypes between drug profiles represent the potential effects of drug pairs and the drug pairs with strong potential effects are more likely to interact. When applied to drug interactions with verified effects, both therapeutic and adverse effects have been successfully identified with high specificity and sensitivity. Finally, tracing drug interactions in molecular and phenotypic networks allows us to understand the MoA.
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Registered trials
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.