ArticleChemical research in toxicology2021
Trade-off Predictivity and Explainability for Machine-Learning Powered Predictive Toxicology: An in-Depth Investigation with Tox21 Data Sets.
Article in Chemical research in toxicology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: a systematic review.BMJ health & care informatics · 2024Pooled it
- Tox21mer, A transformer foundation model for Tox21 high-throughput concentration-response curves data.bioRxiv : the preprint server for biology · 2026Article
- A pipeline for developing AI-driven models to predict molecular initiating events: a case study on neural tube defects.Journal of cheminformatics · 2026Article
- Development of machine learning-based multi-task quantitative structure-activity relationship models for predicting toxicities in six human organ systems.Computational toxicology (Amsterdam, Netherlands) · 2026Article
- Hierarchical Mechanistic Modeling of Complex Toxicity Endpoints from Public Concentration-Response Data.Environmental science & technology · 2026Article
- Comparative Study of Molecular Descriptors and AI-Based Embeddings for Toxicity Prediction.Chemical research in toxicology · 2025Article
- Beyond QSARs: Quantitative Knowledge-Activity Relationships (QKARs) for enhanced drug toxicity prediction.Toxicological sciences : an official journal of the Society of Toxicology · 2025Article
- Improved Interpretability Without Performance Reduction in a Sepsis Prediction Risk Score.Diagnostics (Basel, Switzerland) · 2025Article
- Hybrid Class Balancing Approach for Chemical Compound Toxicity Prediction.Current computer-aided drug design · 2025Article
- Novel target identification towards drug repurposing based on biological activity profiles.PloS one · 2025Article
- Article
- VenomPred 2.0: A NovelJournal of chemical information and modeling · 2024Article
- Interpreting drug synergy in breast cancer with deep learning using target-protein inhibition profiles.BioData mining · 2024Article
- PERform: assessing model performance with predictivity and explainability readiness formula.Journal of environmental science and health. Part C, Toxicology and carcinogenesis · 2024Article
- Artificial intelligence integration in the drug lifecycle and in regulatory science: policy implications, challenges and opportunities.Frontiers in pharmacology · 2024Article
- Prediction of Drug Targets based on In Vitro Activity Profiles Toward Drug Repurposing for Rare Diseases.IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine · 2023Article
- Modernizing persistence-bioaccumulation-toxicity (PBT) assessment with high throughput animal-free methods.Archives of toxicology · 2023Article
- Evaluating the utility of a high throughput thiol-containing fluorescent probe to screen for reactivity: A case study with the Tox21 library.Computational toxicology (Amsterdam, Netherlands) · 2023Article
- Molecular Property Prediction by Combining LSTM and GAT.Biomolecules · 2023Article
- Article
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Authors and funding
6 authors.
Funding
Abstract
Selecting a model in predictive toxicology often involves a trade-off between prediction performance and explainability: should we sacrifice the model performance to gain explainability or vice versa. Here we present a comprehensive study to assess algorithm and feature influences on model performance in chemical toxicity research. We conducted over 5000 models for a Tox21 bioassay data set of 65 assays and ∼7600 compounds. Seven molecular representations as features and 12 modeling approaches varying in complexity and explainability were employed to systematically investigate the impact of various factors on model performance and explainability. We demonstrated that end points dictated a model's performance, regardless of the chosen modeling approach including deep learning and chemical features. Overall, more complex models such as (LS-)SVM and Random Forest performed marginally better than simpler models such as linear regression and KNN in the presented Tox21 data analysis. Since a simpler model with acceptable performance often also is easy to interpret for the Tox21 data set, it clearly was the preferred choice due to its better explainability. Given that each data set had its own error structure both for dependent and independent variables, we strongly recommend that it is important to conduct a systematic study with a broad range of model complexity and feature explainability to identify model balancing its predictivity and explainability.
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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.