ArticleLife science alliance2018
An integrative machine learning approach for prediction of toxicity-related drug safety.
Article in Life science alliance, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed.
- Target-biology and interactome-derived signatures predict target-level associations with safety-related drug attrition.Scientific reports · 2026Article
- A scoping review of artificial intelligence applications in clinical trial risk assessment.NPJ digital medicine · 2025Article
- A Multi-Modal Graph Neural Network Framework for Parkinson's Disease Therapeutic Discovery.International journal of molecular sciences · 2025Article
- Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models.Communications chemistry · 2025Article
- Role of Artificial Intelligence in Drug Discovery to Revolutionize the Pharmaceutical Industry: Resources, Methods and Applications.Recent patents on biotechnology · 2025Review
- Recent Development, Applications, and Patents of Artificial Intelligence in Drug Design and Development.Current drug discovery technologies · 2025Review
- Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction.Chemical research in toxicology · 2024Review
- A comprehensive overview of advanced dynamicTissue barriers · 2024Review
- Predicting clinical trial success forFrontiers in artificial intelligence · 2024Article
- Revolutionizing adjuvant development: harnessing AI for next-generation cancer vaccines.Frontiers in immunology · 2024Review
- Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics.Current pharmaceutical design · 2024Review
- Artificial intelligence integration in the drug lifecycle and in regulatory science: policy implications, challenges and opportunities.Frontiers in pharmacology · 2024Article
- Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-Based Featurization Method.Journal of chemical information and modeling · 2023Article
- Federated Learning in Computational Toxicology: An Industrial Perspective on the Effiris Hackathon.Chemical research in toxicology · 2023Article
- The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies.Pharmaceuticals (Basel, Switzerland) · 2023Review
- Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies.Molecules (Basel, Switzerland) · 2023Review
- The evolving role of investigative toxicology in the pharmaceutical industry.Nature reviews. Drug discovery · 2023Review
- In-vivo processing of nanoassemblies: a neglected framework for recycling to bypass nanotoxicological therapeutics.Toxicology research · 2023Review
- Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare.Biosensors · 2022Review
- Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery.Molecular diversity · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent trends in drug development have been marked by diminishing returns caused by the escalating costs and falling rates of new drug approval. Unacceptable drug toxicity is a substantial cause of drug failure during clinical trials and the leading cause of drug withdraws after release to the market. Computational methods capable of predicting these failures can reduce the waste of resources and time devoted to the investigation of compounds that ultimately fail. We propose an original machine learning method that leverages identity of drug targets and off-targets, functional impact score computed from Gene Ontology annotations, and biological network data to predict drug toxicity. We demonstrate that our method (TargeTox) can distinguish potentially idiosyncratically toxic drugs from safe drugs and is also suitable for speculative evaluation of different target sets to support the design of optimal low-toxicity combinations.
Identifiers
What OpenQuestion holds
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.