Benchmarking ML in ADMET predictions: the practical impact of feature representations in ligand-based models

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.