Machine Learning guided early drug discovery of small molecules

Machine learning (ML) approaches have been widely adopted within the early stages of the drug discovery process, particularly within the context of small-molecule drug candidates. Despite this, the use of ML is still limited in the pharmacokinetic/pharmacodynamic (PK/PD) application space. Here, we describe recent progress and the role of ML used in preclinical drug discovery. We summarize the advances and current strategies used to predict ADME (absorption, distribution, metabolism and, excretion) properties of small molecules based on their structures, and predict structures based on the desired properties for molecular screening and optimization. Finally, we discuss the use of ML to predict PK to rank the ability of drug candidates to achieve appropriate exposures and hence provide important insights into safety and efficacy.

Automated summary

Machine learning has been widely used in the early stages of drug discovery, but its applications in pharmacokinetic/pharmacodynamic (PK/PD) field are still limited. Recent progress in ML has focused on predicting ADME properties of small molecules and PK of drug candidates, providing important insights into safety and efficacy.