Pharma
Artificial Intelligence and AutoML for Predicting Human Intestinal Absorption: A QSPR Study on Serotonergic Compounds
- machine learning
- artificial intelligence
- AutoML
- quantitative structure property relationship
- QSPR
- human intestinal absorption
- drug discovery
- predictive modeling
- explainable AI
- SHAP
MLJAR tools were used in the following publication.
Artificial Intelligence-Based Quantitative Structure–Property Relationship Model for Predicting Human Intestinal Absorption of Compounds with Serotonergic Activity
Natalia Czub, Jakub Szlęk, Adam Pacławski, Klaudia Klimonczyk, Matteo Puccetti, Aleksander Mendyk
Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, Kraków, Poland | Department of Pharmaceutical Sciences, University of Perugia, Perugia, Italy
This peer-reviewed study presents an artificial intelligence and AutoML-based QSPR framework for predicting human intestinal absorption (HIA) of serotonergic drug candidates. The authors developed a two-stage modeling pipeline combining classification and regression models to improve prediction reliability for oral drug screening. Built with automated machine learning and validated using cross-validation and external datasets, the system demonstrates strong performance in identifying highly permeable compounds. The research highlights how machine learning, predictive analytics, and explainable AI can support early-stage drug discovery and ADME analysis.
Molecular Pharmaceutics • April 5, 2023
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