  Machine learning (ML) is revolutionising drug discovery by expediting the
prediction of small molecule properties essential for developing new drugs.
These properties -- including absorption, distribution, metabolism and
excretion (ADME)-- are crucial in the early stages of drug development since
they provide an understanding of the course of the drug in the organism, i.e.,
the drug's pharmacokinetics. However, existing methods lack personalisation and
rely on manually crafted ML algorithms or pipelines, which can introduce
inefficiencies and biases into the process. To address these challenges, we
propose a novel evolutionary-based automated ML method (AutoML) specifically
designed for predicting small molecule properties, with a particular focus on
pharmacokinetics. Leveraging the advantages of grammar-based genetic
programming, our AutoML method streamlines the process by automatically
selecting algorithms and designing predictive pipelines tailored to the
particular characteristics of input molecular data. Results demonstrate
AutoML's effectiveness in selecting diverse ML algorithms, resulting in
comparable or even improved predictive performances compared to conventional
approaches. By offering personalised ML-driven pipelines, our method promises
to enhance small molecule research in drug discovery, providing researchers
with a valuable tool for accelerating the development of novel therapeutic
drugs.
