ReviewACS omega2022
Machine Learning Toxicity Prediction: Latest Advances by Toxicity End Point.
Review in ACS omega, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 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
73 citing papers in PubMed.
- Target-biology and interactome-derived signatures predict target-level associations with safety-related drug attrition.Scientific reports · 2026Article
- A review of machine learning in toxicology: current practices and reporting gaps.Archives of toxicology · 2026Review
- AI/ML-based computational models for toxicity prediction.Environmental science and pollution research international · 2026Review
- Research Progress and Application Prospects of Bioaffinity Chromatography in Screening Cardiotoxic Components of Chinese Herbal Medicines.ACS omega · 2026Review
- Unlabeled but Not Unseen: Cytotoxicity Classification of Re(I) Tricarbonyl Complexes via K‑Means Clustering.ACS omega · 2026Article
- Beyond Molecular Structures: Investigating Demographic Factors in Drug-Induced Cardiotoxicity Prediction Models.Journal of chemical information and modeling · 2026Article
- Tox21mer, A transformer foundation model for Tox21 high-throughput concentration-response curves data.bioRxiv : the preprint server for biology · 2026Article
- Class-level DILI saturation and hERG-driven risk stratification among 2024-2025 FDA-approved kinase inhibitors: an in silico multi-platform safety analysis.Investigational new drugs · 2026Article
- Review
- Advances in the application of computational toxicology in cosmetic safety assessment.Archives of toxicology · 2026Review
- Unveiling molecular signatures for precision drug design: machine learning insights from trypanothione reductase, PKC-θ, and CB1.Molecular diversity · 2026Article
- Integrated Computer-Aided Drug Design: Advances in GPCR Natural Ligand Discovery.Cell biochemistry and biophysics · 2026Review
- Machine learning-guided Nano-QSAR modeling predicts HepaRG cell membrane toxicity of engineered nanoparticles with mechanistic insights.Cell biology and toxicology · 2026Article
- Targeting IDH-driven Oncometabolism in glioblastoma: a computational approach for the identification of potential inhibitors.Frontiers in bioinformatics · 2026Article
- A Metabolism-Informed Neural Network Identifies Pathways Influencing the Potency and Toxicity of Antimicrobial Combinations.npj drug discovery · 2026Article
- Artificial Intelligence and Machine Learning-Based Approaches for Genetic Damage Prediction.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Shiny binary modeling platform: a reproducibility-oriented software platform for structured binary modeling with integrated interpretation, validation support, model comparison, and archive export.Frontiers in pharmacology · 2026Article
- Review
- Establishment of an Integrated Model for Predicting Compound Mutagenicity with a Feature Importance Analysis.Journal of chemical information and modeling · 2025Article
- Alternative methods for developmental toxicity testing: features, status and future directions.Journal of veterinary science · 2025Review
13 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) models to predict the toxicity of small molecules have garnered great attention and have become widely used in recent years. Computational toxicity prediction is particularly advantageous in the early stages of drug discovery in order to filter out molecules with high probability of failing in clinical trials. This has been helped by the increase in the number of large toxicology databases available. However, being an area of recent application, a greater understanding of the scope and applicability of ML methods is still necessary. There are various kinds of toxic end points that have been predicted
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.