ArticleJournal of cheminformatics2019
SAR and QSAR modeling of a large collection of LD
Article in Journal of cheminformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 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
50 citing papers in PubMed.
- Trial
- Multi-Target Bioactivity ofPharmaceuticals (Basel, Switzerland) · 2026Article
- From structural features to mouse acute intraperitoneal toxicity prediction: a triple computational toxicology approach for safety assessment of flavonoids.Molecular diversity · 2026Article
- Interpretable QSAR models for acute oral toxicity via tuned XGBoost and hybrid data sampling techniques.Scientific reports · 2026Article
- Assessing the performance of quantum-mechanical descriptors in physicochemical and biological property prediction.Digital discovery · 2026Article
- In Silico Prediction of Potential pTLR7/pSTING Dual-Targeting Ligands via Virtual Screening and Molecular Dynamics Simulation.International journal of molecular sciences · 2025Article
- Evaluation of selected indigenous spices- and herbs-derived small molecules as potential inhibitors of SREBP and its implications for breast cancer using MD simulations and MMPBSA calculations.Molecular diversity · 2025Article
- Dual-action nanotherapy: Temozolomide-loaded, anti-PD-L1 scFv-functionalized lipid nanocarriers for targeted glioblastoma therapy.European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences · 2025Article
- ADMET-Guided Docking and GROMACS Molecular Dynamics ofPharmaceuticals (Basel, Switzerland) · 2025Article
- Application of in silico methods to predict the acute toxicity of bicyclic organophosphorus compounds as potential chemical weapon.Archives of toxicology · 2025Article
- Overcoming Clusterin-Induced Chemoresistance in Cancer: A Computational Study Using a Fragment-Based Drug Discovery Approach.Biology · 2025Article
- Role of artificial intelligence in revolutionizing drug discovery.Fundamental research · 2025Review
- ApisTox: a new benchmark dataset for the classification of small molecules toxicity on honey bees.Scientific data · 2025Article
- In silico evaluation and therapeutic targeting of LVDD9B protein for WSSV inhibition: molecular and ecological insights for aquaculture solutions.In silico pharmacology · 2025Article
- Computational exploration of aphrodisiac potential: identifying inhibitors fromIn silico pharmacology · 2025Article
- Identification and therapeutic investigation of biomarker genes underpinning hepatocellular carcinoma: anFrontiers in bioinformatics · 2025Article
- Comprehensive overview of the toxicities of small-molecule cryoprotectants for carnivorous spermatozoa: foundation for computational cryobiotechnology.Frontiers in toxicology · 2025Review
- Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction.Chemical research in toxicology · 2024Review
- Transfer learning for a foundational chemistry model.Chemical science · 2024Article
- A Comparative Analysis on the Potential Anticancer Properties of Tetrahydrocannabinol, Cannabidiol, and Tetrahydrocannabivarin Compounds Through In Silico Approach.Asian Pacific journal of cancer prevention : APJCP · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
The median lethal dose for rodent oral acute toxicity (LD50) is a standard piece of information required to categorize chemicals in terms of the potential hazard posed to human health after acute exposure. The exclusive use of in vivo testing is limited by the time and costs required for performing experiments and by the need to sacrifice a number of animals. (Quantitative) structure-activity relationships [(Q)SAR] proved a valid alternative to reduce and assist in vivo assays for assessing acute toxicological hazard. In the framework of a new international collaborative project, the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods and the U.S. Environmental Protection Agency's National Center for Computational Toxicology compiled a large database of rat acute oral LD50 data, with the aim of supporting the development of new computational models for predicting five regulatory relevant acute toxicity endpoints. In this article, a series of regression and classification computational models were developed by employing different statistical and knowledge-based methodologies. External validation was performed to demonstrate the real-life predictability of models. Integrated modeling was then applied to improve performance of single models. Statistical results confirmed the relevance of developed models in regulatory frameworks, and confirmed the effectiveness of integrated modeling. The best integrated strategies reached RMSEs lower than 0.50 and the best classification models reached balanced accuracies over 0.70 for multi-class and over 0.80 for binary endpoints. Computed predictions will be hosted on the EPA's Chemistry Dashboard and made freely available to the scientific community.
Indexed as
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.