Benchmarking ML in ADMET predictions: A focus on hypothesis testing practices

13 March 2024, Version 3
This content is a preprint and has not undergone peer review at the time of posting.

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. This approach aims to bolster the reliability of ADMET predictions, providing more dependable and informative model evaluations.

Keywords

Statistical Model Evaluation
Cross-Validation
ADME(T) Prediction
Ligand-Based Modeling

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