ArticlePharmaceutics2022
Reliable Prediction of Caco-2 Permeability by Supervised Recursive Machine Learning Approaches.
Article in Pharmaceutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Reliable Apparent Permeability Prediction Through Benchmarking, Stability, Uncertainty Quantification, and Explainable Machine Learning.CPT: pharmacometrics & systems pharmacology · 2026Article
- Application of New Approach Methodologies to Improve Oral Biopharmaceutic Assessments.Pharmaceutics · 2026Review
- CaliciBoost: Performance-driven evaluation of molecular representations for caco-2 permeability prediction.Journal of cheminformatics · 2025Article
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Prediction of blood-brain barrier and Caco-2 permeability through the Enalos Cloud Platform: combining contrastive learning and atom-attention message passing neural networks.Journal of cheminformatics · 2025Article
- hERG toxicity prediction in early drug discovery using extreme gradient boosting and isometric stratified ensemble mapping.Scientific reports · 2025Article
- Novel 2-Thiazolylhydrazone with Druggable Properties for Antifungal Application.Journal of fungi (Basel, Switzerland) · 2025Article
- ADMET evaluation in drug discovery: 21. Application and industrial validation of machine learning algorithms for Caco-2 permeability prediction.Journal of cheminformatics · 2025Article
- FormulationBCS: A Machine Learning Platform Based on Diverse Molecular Representations for Biopharmaceutical Classification System (BCS) Class Prediction.Molecular pharmaceutics · 2025Article
- Explainable Artificial Intelligence in the Field of Drug Research.Drug design, development and therapy · 2025Review
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- Exploring putative enteric methanogenesis inhibitors using molecular simulations and a graph neural network.bioRxiv : the preprint server for biology · 2024Article
- Article
- Review
- Betulin Acid Ester Derivatives Inhibit Cancer Cell Growth by Inducing Apoptosis through Caspase Cascade Activation: A Comprehensive In Vitro and In Silico Study.International journal of molecular sciences · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The heterogeneity of the Caco-2 cell line and differences in experimental protocols for permeability assessment using this cell-based method have resulted in the high variability of Caco-2 permeability measurements. These problems have limited the generation of large datasets to develop accurate and applicable regression models. This study presents a QSPR approach developed on the KNIME analytical platform and based on a structurally diverse dataset of over 4900 molecules. Interpretable models were obtained using random forest supervised recursive algorithms for data cleaning and feature selection. The development of a conditional consensus model based on regional and global regression random forest produced models with RMSE values between 0.43-0.51 for all validation sets. The potential applicability of the model as a surrogate for the in vitro Caco-2 assay was demonstrated through blind prediction of 32 drugs recommended by the International Council for the Harmonization of Technical Requirements for Pharmaceuticals (ICH) for validation of in vitro permeability methods. The model was validated for the preliminary estimation of the BCS/BDDCS class. The KNIME workflow developed to automate new drug prediction is freely available. The results suggest that this automated prediction platform is a reliable tool for identifying the most promising compounds with high intestinal permeability during the early stages of 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.