Interpreting Machine Learning Pipelines Produced by Evolutionary AutoML for Biochemical Property Prediction

Abstract

Machine learning (ML) has been playing a crucial role in drug discovery, mainly through quantitative structure-activity relationship models that relate molecular structures to properties, such as absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. However, traditional ML approaches often lack customisation to a particular biochemical task and fail to generalise to new biochemical spaces, resulting in reduced predictive performance. Automated machine learning (AutoML) has emerged to address these limitations by automatically selecting the suitable ML pipelines for a given input dataset. Despite its potential, AutoML is underutilised in cheminformatics, and its de..