ArticleJournal of cheminformatics2025
Benchmarking ML in ADMET predictions: the practical impact of feature representations in ligand-based models.
Article in Journal of cheminformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- ADMET-XSpec: A Tool for Systematic Cross-Species Data Integration in ADMET Prediction.Chemical research in toxicology · 2026Article
- An End-User Audit of Reproducibility, Data Leakage, and Overfitting of the Top-Ranked ADMET Prediction Models in TDC Leaderboards.Journal of chemical information and modeling · 2026Article
- The Tsetlin Machine: A "Third Way" in QSAR Modeling.Journal of chemical information and modeling · 2026Article
- Revisiting ADMET prediction reliability under real-world challenges in the foundation model era.Journal of cheminformatics · 2026Article
- Toward trustworthy artificial intelligence in multi-omics: a review of reproducibility, stability, and interpretability.Briefings in bioinformatics · 2026Review
- Predicting toxicity and bioactivity of the chemical exposome: a case study for the blood exposome database.Journal of cheminformatics · 2026Article
- Data curation in cheminformatics: importance and implementation.Journal of cheminformatics · 2026Review
- Scaling Biomedical Text-Mining: Transformers, GenAI, and Drug Discovery.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Predicting Toxicity and Bioactivity of the Chemical Exposome: A Case Study for the Blood Exposome Database.bioRxiv : the preprint server for biology · 2025Article
- Conceptual density functional theory in drug discovery: an overview.Journal of molecular modeling · 2025Review
- Advancing ADMET prediction through multiscale fragment-aware pretraining with MSformer-ADMET.Briefings in bioinformatics · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study, focusing on predicting Absorption, Distribution, Metabolism, Excretion, and Toxicology (ADMET) properties, addresses the key challenges of ML models trained using ligand-based representations. We propose a structured approach to data feature selection, taking a step beyond the conventional practice of combining different representations without systematic reasoning. Additionally, we enhance model evaluation methods by integrating cross-validation with statistical hypothesis testing, adding a layer of reliability to the model assessments. Our final evaluations include a practical scenario, where models trained on one source of data are evaluated on a different one. This approach aims to bolster the reliability of ADMET predictions, providing more dependable and informative model evaluations.Scientific contributionThis study provided a structured approach to feature selection. We improve model evaluation by combining cross-validation with statistical hypothesis testing, making results more reliable. The methodology used in our study can be generalized beyond feature selection, boosting the confidence in selected models which is crucial in a noisy domain such as the ADMET prediction tasks. Additionally, we assess how well models trained on one dataset perform on another, offering practical insights for using external data in drug discovery.
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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.