ArticleJournal of cheminformatics2021
Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network.
Article in Journal of cheminformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- AI/ML-based computational models for toxicity prediction.Environmental science and pollution research international · 2026Review
- AI-Driven Design Platforms of Next-Generation Antibody Therapeutics.Topics in current chemistry (Cham) · 2026Review
- Review
- Article
- CKAN-ATHP: a predictor for antihypertensive peptides based on sample augmentation and loss improvement strategies using the convolutional Kolmogorov-Arnold network.Journal of cheminformatics · 2026Article
- A review on integrated machine learning and deep learning driven artificial intelligence models for pharmacokinetics and toxicokinetics predictions, and their application.Drug metabolism and disposition: the biological fate of chemicals · 2026Review
- Navigating the AI Frontier in Toxicology: Trends, Trust, and Transformation.Current environmental health reports · 2025Review
- Silver integrated hybrids and nanocomposites for next-generation biomedicine: Beyond antimicrobial coatings toward smart sense-response-heal platforms.Materials today. Bio · 2025Review
- Beyond QSARs: Quantitative Knowledge-Activity Relationships (QKARs) for enhanced drug toxicity prediction.Toxicological sciences : an official journal of the Society of Toxicology · 2025Article
- Graph Neural Network-Based Toxicity Prediction by Integrating Molecular Fingerprints and Knowledge Graph Features.Toxics · 2025Article
- Qsarna: An Online Tool for Smart Chemical Space Navigation in Drug Design.Journal of chemical information and modeling · 2025Article
- A multi-task graph deep learning model to predict drugs combination of synergy and sensitivity scores.BMC bioinformatics · 2024Article
- The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges.Molecular therapy. Nucleic acids · 2024Review
- Machine Learned Classification of Ligand Intrinsic Activities at Human μ-Opioid Receptor.ACS chemical neuroscience · 2024Article
- Machine Learned Classification of Ligand Intrinsic Activities at HumanbioRxiv : the preprint server for biology · 2024Article
- Review
- VenomPred 2.0: A NovelJournal of chemical information and modeling · 2024Article
- A comprehensive review of artificial intelligence for pharmacology research.Frontiers in genetics · 2024Review
- Review of machine learning and deep learning models for toxicity prediction.Experimental biology and medicine (Maywood, N.J.) · 2023Review
- DeepBindGCN: Integrating Molecular Vector Representation with Graph Convolutional Neural Networks for Protein-Ligand Interaction Prediction.Molecules (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
As safety is one of the most important properties of drugs, chemical toxicology prediction has received increasing attentions in the drug discovery research. Traditionally, researchers rely on in vitro and in vivo experiments to test the toxicity of chemical compounds. However, not only are these experiments time consuming and costly, but experiments that involve animal testing are increasingly subject to ethical concerns. While traditional machine learning (ML) methods have been used in the field with some success, the limited availability of annotated toxicity data is the major hurdle for further improving model performance. Inspired by the success of semi-supervised learning (SSL) algorithms, we propose a Graph Convolution Neural Network (GCN) to predict chemical toxicity and trained the network by the Mean Teacher (MT) SSL algorithm. Using the Tox21 data, our optimal SSL-GCN models for predicting the twelve toxicological endpoints achieve an average ROC-AUC score of 0.757 in the test set, which is a 6% improvement over GCN models trained by supervised learning and conventional ML methods. Our SSL-GCN models also exhibit superior performance when compared to models constructed using the built-in DeepChem ML methods. This study demonstrates that SSL can increase the prediction power of models by learning from unannotated data. The optimal unannotated to annotated data ratio ranges between 1:1 and 4:1. This study demonstrates the success of SSL in chemical toxicity prediction; the same technique is expected to be beneficial to other chemical property prediction tasks by utilizing existing large chemical databases. Our optimal model SSL-GCN is hosted on an online server accessible through: https://app.cbbio.online/ssl-gcn/home .
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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 Socratic graph.