ArticleNucleic acids research2023
TOXRIC: a comprehensive database of toxicological data and benchmarks.
Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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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.
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Who cites it
35 citing papers in PubMed.
- Biopesticidal Potential of theInternational journal of molecular sciences · 2026Article
- Task-adaptive multimodal molecular representations for structure-sensitive property prediction.Chemical science · 2026Article
- ToxiGuard: an AOP-guided mechanistically interpretable framework for multi-organ toxicity prediction.Archives of toxicology · 2026Article
- ToxiVerse: chemical bioprofiling, toxicity data sharing and customizable predictive modeling.Bioinformatics (Oxford, England) · 2026Article
- AI/ML-based computational models for toxicity prediction.Environmental science and pollution research international · 2026Review
- Comparative evaluation and selection of optimal QSAR-based machine learning model for liver toxicity prediction.Molecular diversity · 2026Article
- Integrated assessment of developmental toxicity of antibiotic pollutants: Machine learning prediction, zebrafish validation, and network toxicology.Molecular diversity · 2026Article
- A Multitask Active Learning Framework with Probabilistic Modeling for Multi-Species Acute Toxicity Prediction.Molecules (Basel, Switzerland) · 2026Article
- ToxiVerse: A Public Platform for Chemical Toxicity Data Sharing and Customizable Predictive Modeling.bioRxiv : the preprint server for biology · 2026Article
- De novo covalent drug generation with enhanced drug-likeness and safety.Communications biology · 2026Article
- NPASS database update 2026: comprehensive quantitative composition, bioactivity, and ADME-Tox data of natural products for biomedical research.Nucleic acids research · 2026Article
- Multi-Task Cascade Forest Framework for Predicting Acute Toxicity across Species.Research (Washington, D.C.) · 2026Article
- SM-GAT: a safety-aware multi-task graph attention network for multi-target anti-diabetic lead discovery from natural products.Frontiers in molecular biosciences · 2026Article
- Structural Isomer Cumulative molecular fingerprinting method (SIC) for standardizing structural isomeric relationships.Communications chemistry · 2025Article
- ToxAssay: a hierarchical model-driven tool for advanced toxicogenomics biomarker discovery.Bioinformatics (Oxford, England) · 2025Article
- Computational toxicology in drug discovery: applications of artificial intelligence in ADMET and toxicity prediction.Briefings in bioinformatics · 2025Review
- HDAC3_VS_assistant: cheminformatics-driven discovery of histone deacetylase 3 inhibitors.Molecular diversity · 2025Article
- Impact of halogenation on scaffold toxicity assessed using HD-GEM machine learning model.Briefings in bioinformatics · 2025Article
- ToxACoL: an endpoint-aware and task-focused compound representation learning paradigm for acute toxicity assessment.Nature communications · 2025Article
- Artificial Intelligence-Driven Drug Toxicity Prediction: Advances, Challenges, and Future Directions.Toxics · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The toxic effects of compounds on environment, humans, and other organisms have been a major focus of many research areas, including drug discovery and ecological research. Identifying the potential toxicity in the early stage of compound/drug discovery is critical. The rapid development of computational methods for evaluating various toxicity categories has increased the need for comprehensive and system-level collection of toxicological data, associated attributes, and benchmarks. To contribute toward this goal, we proposed TOXRIC (https://toxric.bioinforai.tech/), a database with comprehensive toxicological data, standardized attribute data, practical benchmarks, informative visualization of molecular representations, and an intuitive function interface. The data stored in TOXRIC contains 113 372 compounds, 13 toxicity categories, 1474 toxicity endpoints covering in vivo/in vitro endpoints and 39 feature types, covering structural, target, transcriptome, metabolic data, and other descriptors. All the curated datasets of endpoints and features can be retrieved, downloaded and directly used as output or input to Machine Learning (ML)-based prediction models. In addition to serving as a data repository, TOXRIC also provides visualization of benchmarks and molecular representations for all endpoint datasets. Based on these results, researchers can better understand and select optimal feature types, molecular representations, and baseline algorithms for each endpoint prediction task. We believe that the rich information on compound toxicology, ML-ready datasets, benchmarks and molecular representation distribution can greatly facilitate toxicological investigations, interpretation of toxicological mechanisms, compound/drug discovery and the development of computational methods.
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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 Socratic graph.