ArticleACS omega2024
Identification of Optimal Machine Learning Algorithms and Molecular Fingerprints for Explainable Toxicity Prediction Models Using ToxCast/Tox21 Bioassay Data.
Article in ACS omega, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Data-Driven Critical Evaluation of the General Solubility Equation.Journal of chemical information and modeling · 2026Article
- Learning the assay, not the hazard: how computational toxicity models inherit the artifacts of their high-throughput training data.Archives of toxicology · 2026Article
- Molecular Representation and Closed-Loop Validation for Toxicity Assessment of Organic Compounds in Ambient Air PMEnvironmental science & technology · 2026Article
- ChronicDPipredictor: an interpretable deep learning framework for chemical chronic and subchronic toxicity assessment.Molecular diversity · 2026Article
- Predicting peroxisome proliferator-activated receptor gamma potency of small molecules: a synergistic consensus model and deep learning binding affinity approach powered by Enalos Cloud Platform.Molecular diversity · 2026Article
- Multi-task deep learning models for mechanism-based prediction of developmental and reproductive toxicity (DART) using ToxCast bioassays.Frontiers in toxicology · 2026Article
- A Machine Learning-Empowered Quantitative Structure-Activity Relationship Model for Predicting the Plasma Half-life of Drugs in Dogs.The AAPS journal · 2025Article
- Structure-Activity Relationships and Design of Focused Libraries Tailored for Staphylococcus Aureus Inhibition.Molecular informatics · 2025Article
- Big data and AI: Potential and challenges for digital transformation in toxicology.Environmental analysis, health and toxicology · 2025Article
- The Missing Enzymes: A Call to Update Pharmacological Profiling Practices for Better Drug Safety Assessment.Journal of medicinal chemistry · 2025Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent studies have primarily focused on introducing novel frameworks to enhance the predictive power of toxicity prediction models by refining molecular representation methods and algorithms. However, these methods are inherently complex and often pose challenges in understanding and explaining, leading to barriers in their regulatory adoption and validation. Therefore, it is necessary to select the optimal model, considering not only model performance but also interpretability. This study aimed to identify the optimal combination of molecular fingerprints (pattern-based versus algorithm-based) and machine learning algorithms (simple versus complex) for developing explainable toxicity prediction models through an comprehensive investigation of the ToxCast/Tox21 bioassay data set. For 1092 ToxCast/Tox21 assays, five molecular fingerprints (MACCS, Morgan, RDKit, Layered, and Patterned) and six algorithms (MLP, GBT, Random Forest,
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.