ArticleEnvironmental science & technology2022
Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data.
Article in Environmental science & technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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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.
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
18 citing papers in PubMed.
- ToxiVerse: chemical bioprofiling, toxicity data sharing and customizable predictive modeling.Bioinformatics (Oxford, England) · 2026Article
- Applications of Stem Cells and Modern Toxicological Analytical Methods in the Toxicity of Microplastics.Toxics · 2026Review
- Collision-free morgan fingerprints: a principled approach to enhance machine learning performance and interpretability in chemistry.Journal of cheminformatics · 2026Article
- ToxiVerse: A Public Platform for Chemical Toxicity Data Sharing and Customizable Predictive Modeling.bioRxiv : the preprint server for biology · 2026Article
- Hierarchical Mechanistic Modeling of Complex Toxicity Endpoints from Public Concentration-Response Data.Environmental science & technology · 2026Article
- Developmental toxicity: artificial intelligence-powered assessments.Trends in pharmacological sciences · 2025Review
- Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.Chemical research in toxicology · 2025Review
- Development of hybrid models by the integration of the read-across hypothesis with the QSAR framework for the assessment of developmental and reproductive toxicity (DART) tested according to OECD TG 414.Toxicology reports · 2024Article
- A novel multitask learning algorithm for tasks with distinct chemical space: zebrafish toxicity prediction as an example.Journal of cheminformatics · 2024Article
- Predicting Chemical Immunotoxicity through Data-Driven QSAR Modeling of Aryl Hydrocarbon Receptor Agonism and Related Toxicity Mechanisms.Environment & health (Washington, D.C.) · 2024Article
- Hybrid non-animal modeling: A mechanistic approach to predict chemical hepatotoxicity.Journal of hazardous materials · 2024Article
- Fetus Exposure to Drugs and Chemicals: A Holistic Overview on the Assessment of Their Transport and Metabolism across the Human Placental Barrier.Diseases (Basel, Switzerland) · 2024Review
- Data-Driven Quantitative Structure-Activity Relationship Modeling for Human Carcinogenicity by Chronic Oral Exposure.Environmental science & technology · 2023Article
- Human-Based New Approach Methodologies in Developmental Toxicity Testing: A Step Ahead from the State of the Art with a Feto-Placental Organ-on-Chip Platform.International journal of environmental research and public health · 2022Review
- Prediction of drug-induced liver injury and cardiotoxicity using chemical structure and in vitro assay data.Toxicology and applied pharmacology · 2022Article
- Machine Learning and Artificial Intelligence in Toxicological Sciences.Toxicological sciences : an official journal of the Society of Toxicology · 2022Review
- Prioritization of chemicals in food for risk assessment by integrating exposure estimates and new approach methodologies: A next generation risk assessment case study.Frontiers in toxicology · 2022Article
- Computational model for fetal skeletal defects potentially linked to disruption of retinoic acid signaling.Frontiers in pharmacology · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
For hazard identification, classification, and labeling purposes, animal testing guidelines are required by law to evaluate the developmental toxicity potential of new and existing chemical products. However, guideline developmental toxicity studies are costly, time-consuming, and require many laboratory animals. Computational modeling has emerged as a promising, animal-sparing, and cost-effective method for evaluating the developmental toxicity potential of chemicals, such as endocrine disruptors, without the use of animals. We aimed to develop a predictive and explainable computational model for developmental toxicants. To this end, a comprehensive dataset of 1244 chemicals with developmental toxicity classifications was curated from public repositories and literature sources. Data from 2140 toxicological high-throughput screening assays were extracted from PubChem and the ToxCast program for this dataset and combined with information about 834 chemical fragments to group assays based on their chemical-mechanistic relationships. This effort revealed two assay clusters containing 83 and 76 assays, respectively, with high positive predictive rates for developmental toxicants identified with animal testing guidelines (PPV = 72.4 and 77.3% during cross-validation). These two assay clusters can be used as developmental toxicity models and were applied to predict new chemicals for external validation. This study provides a new strategy for constructing alternative chemical developmental toxicity evaluations that can be replicated for other toxicity modeling studies.
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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.