ArticleJournal of chemical information and modeling2021
GGL-Tox: Geometric Graph Learning for Toxicity Prediction.
Article in Journal of chemical information and modeling, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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.
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Who cites it
15 citing papers in PubMed.
- A geometric graph-based deep learning model for drug-target affinity prediction.BMC bioinformatics · 2025Article
- Multi-Cover Persistence (MCP)-based machine learning for polymer property prediction.Briefings in bioinformatics · 2024Article
- MolLM: a unified language model for integrating biomedical text with 2D and 3D molecular representations.Bioinformatics (Oxford, England) · 2024Article
- Quantum-to-Classical Neural Network Transfer Learning Applied to Drug Toxicity Prediction.Journal of chemical theory and computation · 2024Article
- Interpreting Neural Network Models for Toxicity Prediction by Extracting Learned Chemical Features.Journal of chemical information and modeling · 2024Article
- Gap-Δenergy, a New Metric of the Bond Energy State, Assisting to Predict Molecular Toxicity.ACS omega · 2024Article
- Resibufogenin: An Emerging Therapeutic Compound with Multifaceted Pharmacological Effects - A Comprehensive Review.Medical science monitor : international medical journal of experimental and clinical research · 2024Review
- Machine Learning-Guided Protein Engineering.ACS catalysis · 2023Review
- A Benchmark Study of Graph Models for Molecular Acute Toxicity Prediction.International journal of molecular sciences · 2023Article
- Molecular Toxicity Virtual Screening Applying a Quantized Computational SNN-Based Framework.Molecules (Basel, Switzerland) · 2023Article
- Review
- Predicting drug toxicity at the intersection of informatics and biology: DTox builds a foundation.Patterns (New York, N.Y.) · 2022Article
- Review
- Graph-Based Feature Selection Approach for Molecular Activity Prediction.Journal of chemical information and modeling · 2022Article
- pdCSM-cancer: Using Graph-Based Signatures to Identify Small Molecules with Anticancer Properties.Journal of chemical information and modeling · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Toxicity analysis is a major challenge in drug design and discovery. Recently significant progress has been made through machine learning due to its accuracy, efficiency, and lower cost. US Toxicology in the 21st Century (Tox21) screened a large library of compounds, including approximately 12 000 environmental chemicals and drugs, for different mechanisms responsible for eliciting toxic effects. The Tox21 Data Challenge offered a platform to evaluate different computational methods for toxicity predictions. Inspired by the success of multiscale weighted colored graph (MWCG) theory in protein-ligand binding affinity predictions, we consider MWCG theory for toxicity analysis. In the present work, we develop a geometric graph learning toxicity (GGL-Tox) model by integrating MWCG features and the gradient boosting decision tree (GBDT) algorithm. The benchmark tests of the Tox21 Data Challenge are employed to demonstrate the utility and usefulness of the proposed GGL-Tox model. An extensive comparison with other state-of-the-art models indicates that GGL-Tox is an accurate and efficient model for toxicity analysis and prediction.
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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.