Artificial Intelligence, Machine Learning and Drug Repurposing in Cancer.

Tanoli, Ziaurrehman; Vähä-Koskela, Markus; Aittokallio, Tero

Introduction: Drug repurposing provides a cost-effective strategy to re-use approved drugs for new medical indications. Several machine learning algorithms (ML) and AI approaches have been developed for systematic identification of drug repurposing leads based on big data resources, hence further accelerating and de-risking the drug development process by computational means. Areas covered: The authors focus especially on supervised ML and AI methods that make use of publicly available databases and information resources. While most of the example applications are in the field of anticancer drug therapies, the methods and resources reviewed are widely applicable also to other indications including COVID-19 treatment. A particular emphasis is placed on the use of comprehensive target activity profiles of drugs that enable systematic repurposing process by extending the target profile of a drug to include potent off-targets with therapeutic potential for a new indication. Expert opinion: The scarcity of clinical patient data and the current focus on genetic aberrations as the primary drug targets may limit the performance of anticancer drug repurposing approaches that rely solely on genomics-based information. Functional testing of cancer patient cells exposed to large number of targeted therapies and their combinations provides an additional source of repurposing information for tissue-aware AI approaches.