Artificial Intelligence to Guide Repurposing of Drugs

With the pharmacokinetics, dosing, safety, and manufacturing of approved or investigational drugs already well-characterized, drug repurposing/repositioning offers an emerging strategy to rapidly develop effective treatments for various challenging diseases. However, the growing mass of genetic and multi-omics data has not been effectively explored by the drug repurposing community due to a lack of accurate approaches. This Review aims to be an authoritative, critical, and accessible review and discussion of general interest to the drug repurposing community concerning the use of Artificial Intelligence (AI) and Machine Learning (ML) tools. Emerging questions include what is achievable with AI in this domain and what will the impact be, what does AI/ML embrace, and how can we, as geneticists, pharmacologists, and computational scientists, contribute to the discovery of new, cheap, and affordable repurposable medicines. The fast growth of genetics and multi-omics data (Genomics, Transcriptomics, Proteomics, Metabolomics, and Radiomics) and Electronic Health Records (EHR) in diverse populations contribute to answering questions, such as how to rapidly identify effective repurposable medicines, what is a clinically meaningful effect size in trials, and what are the potential implications for precision medicine. This review will discuss AI/ML for drug repurposing in the context of genetics, multi-omics, real-world data collection, and crowdsourcing of knowledge. We finally conclude by considering questions on how AI/ML methodologies can unite the diverse aspects of translational medicine for emerging treatment development in human challenging diseases.