Towards Evolutionary-based Automated Machine Learning for Small Molecule Pharmacokinetic Prediction

TL;DR

This paper introduces an evolutionary-based AutoML approach tailored for predicting small molecule pharmacokinetics, aiming to improve drug discovery processes by automating and personalizing ML pipeline design.

Contribution

The paper presents a novel grammar-based genetic programming AutoML method specifically designed for small molecule pharmacokinetic prediction, enhancing automation and personalization in drug property modeling.

Findings

AutoML achieves comparable or better predictive performance than traditional methods.

The method effectively automates algorithm selection and pipeline design.

Personalized pipelines improve small molecule property prediction accuracy.

Abstract

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.