ArticleToxicology research2016
QSAR modeling for predicting reproductive toxicity of chemicals in rats for regulatory purposes.
Article in Toxicology research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
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Who cites it
9 citing papers in PubMed.
- Bridging Nano-Interface Interactions and Organ-Specific Toxicity: A Review of Machine Learning for Nanomaterials Risk Assessment.Molecules (Basel, Switzerland) · 2026Review
- A deep-learning approach to predict reproductive toxicity of chemicals using communicative message passing neural network.Frontiers in toxicology · 2025Article
- Comprehensive insights into mechanism of nanotoxicity, assessment methods and regulatory challenges of nanomedicines.Discover nano · 2024Review
- Guidance document on the impact of water treatment processes on residues of active substances or their metabolites in water abstracted for the production of drinking water.EFSA journal. European Food Safety Authority · 2023Article
- Machine learning models for rat multigeneration reproductive toxicity prediction.Frontiers in pharmacology · 2022Article
- Quantitative prediction of repeat dose toxicity values using GenRA.Regulatory toxicology and pharmacology : RTP · 2019Article
- Exploiting machine learning for end-to-end drug discovery and development.Nature materials · 2019Article
- Modeling the pH and temperature dependence of aqueousphase hydroxyl radical reaction rate constants of organic micropollutants using QSPR approach.Environmental science and pollution research international · 2017Article
- QSAR modeling for predicting mutagenic toxicity of diverse chemicals for regulatory purposes.Environmental science and pollution research international · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The experimental determination of multi-generation reproductive toxicity of chemicals involves high costs and a large number of animal studies over a long period of time. Computational toxicology offers possibilities to overcome such difficulties. In this study, we have established ensemble machine learning (EML) based quantitative structure-activity relationship models for predicting the reproductive toxicity potential (LOAEL) of structurally diverse chemicals in accordance with the OECD guidelines. Accordingly, decision tree forest (DTF) and decision tree boost (DTB) QSAR models were developed using a novel dataset composed of the toxicity endpoints for 334 chemicals. Relevant structural features of chemicals responsible for toxicity potential were identified and used in QSAR modeling. The generalization and prediction abilities of the constructed QSAR models were evaluated by internal and external validation procedures and by deriving several stringent statistical criteria parameters. In the test set, the two models (DTF and DTB) yielded
Identifiers
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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.