ArticleJournal of cheminformatics2018
OPERA models for predicting physicochemical properties and environmental fate endpoints.
Article in Journal of cheminformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 230 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
230 citing papers in PubMed.
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- Predicting Aquatic Species Sensitivity Distributions Using Machine Learning in a Regulatory Setting.Chemical research in toxicology · 2026Article
- Beyond Heuristics: A Model-Agnostic Framework for Uncertainty Quantification in QSAR via Adaptive Conformal Prediction.Chemical research in toxicology · 2026Article
- Navigating through toxicological data: review of toxicological databases and sources in the interdisciplinary context including-chemicals, cosmetics, pharmaceuticals and forensics.Archives of toxicology · 2026Review
- Democratizing Artificial Intelligence in Toxicology: Real-World Applications and Automated Computational Workflows.Chemical research in toxicology · 2026Review
- Molecular and structural insights into carvacrol and thymol alkylated derivatives targeting WSSV and AHPND-causing Vibrio parahaemolyticus.Archives of microbiology · 2026Article
- High-throughput PBK modelling for dermal exposure: a pragmatic approach to predict systemic pharmacokinetics.Archives of toxicology · 2026Article
- Extrapolation of in vitro effect concentrations to in vivo bioavailable concentrations using PBK modelling in humans for two classes of persistent and mobile compounds: triazoles and triazines.Archives of toxicology · 2026Article
- Confidently Uncertain: Probabilistic Machine Learning to Predict Soil Biotransformation Half-Lives.Environmental science & technology · 2026Article
- Hazard Assessment of Antioxidants as Contaminants of Concern.Environmental science & technology letters · 2026Article
- Quantitative estimates of inter-individual variability for new approach methodologies-based systemic safety toolbox using a population-based human in vitro model.Toxicological sciences : an official journal of the Society of Toxicology · 2026Article
- Multimodal Cross-Attentive Graph-Based Framework for Predicting In Vivo Endocrine Disruptors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Article
- Application of in vitro to in vivo extrapolation (IVIVE) to inform chemical health guidance value derivation-sample case studies comparing neuro-, hepato-, and developmental toxicities.Toxicological sciences : an official journal of the Society of Toxicology · 2026Article
- Data curation in cheminformatics: importance and implementation.Journal of cheminformatics · 2026Review
- Comparative investigation of the potential of glyphosate and glyphosate-based formulations to cause oxidative stress and DNA damage in human skin and liver cell systems.Toxicological sciences : an official journal of the Society of Toxicology · 2026Article
- G.AI.A: An Integrated Machine-Learning Platform for Predicting Bioaccumulation and Ecotoxicity of Pharmaceuticals.Journal of chemical information and modeling · 2026Article
- Uncertainty-aware machine learning to predict non-cancer human toxicity for the global chemicals market.Nature communications · 2026Article
170 more citing papers are in PubMed but not listed here.
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
The collection of chemical structure information and associated experimental data for quantitative structure-activity/property relationship (QSAR/QSPR) modeling is facilitated by an increasing number of public databases containing large amounts of useful data. However, the performance of QSAR models highly depends on the quality of the data and modeling methodology used. This study aims to develop robust QSAR/QSPR models for chemical properties of environmental interest that can be used for regulatory purposes. This study primarily uses data from the publicly available PHYSPROP database consisting of a set of 13 common physicochemical and environmental fate properties. These datasets have undergone extensive curation using an automated workflow to select only high-quality data, and the chemical structures were standardized prior to calculation of the molecular descriptors. The modeling procedure was developed based on the five Organization for Economic Cooperation and Development (OECD) principles for QSAR models. A weighted k-nearest neighbor approach was adopted using a minimum number of required descriptors calculated using PaDEL, an open-source software. The genetic algorithms selected only the most pertinent and mechanistically interpretable descriptors (2-15, with an average of 11 descriptors). The sizes of the modeled datasets varied from 150 chemicals for biodegradability half-life to 14,050 chemicals for logP, with an average of 3222 chemicals across all endpoints. The optimal models were built on randomly selected training sets (75%) and validated using fivefold cross-validation (CV) and test sets (25%). The CV Q
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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.