Artificial intelligence, machine learning, and drug repurposing in cancer

Abstract

Introduction: Drug repurposing provides a cost-effective strategy to re-use approved drugs for new medical indications. Several machine learning (ML) and artificial intelligence (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 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 that enable a systematic repurposing process by extending the target profile of drugs 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 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 a large number of targeted therapies and their combinations provides an additional source of repurposing information for tissue-aware AI approaches.

Emergence of Cardiac Glycosides as Potential Drugs: Current and Future Scope for Cancer Therapeutics

“…Moreover, ML and its components have been publicized for its rapid and efficient diagnosis, understanding disease dynamics, and repositioning old drugs. Drug repositioning offers many advantages over rational drug discovery, especially to palliate the conventional usage and also to delimit the failure rates in clinical trials [ 186 ]. Scientists have proven that geometric deep learning may assist in forecasting and creating molecular surface interaction fingerprinting to study biomolecular interactions and assist in CG interaction fingerprint with target proteins.…”

An in silico drug repositioning workflow for host-based antivirals

“…Artificial intelligence-based algorithms have been applied in drug repositioning as well as other relevant fields ( Hao et al., 2016 ; Pushpakom et al., 2019 ; Tanoli et al., 2021 ; Wang et al., 2020 ; Yang et al., 2020 ; Zhou et al., 2020 ). This protocol below describes the specific steps of in silico drug repositioning for antivirals against Coronaviridae viral families including SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), SARS-CoV (severe acute respiratory syndrome coronavirus) and MERS-CoV (Middle East respiratory syndrome coronavirus) using Coronaviridae -specific host dependency gene set, refined drug candidate list covering 2457 marketed drugs and 1062 natural compounds, and DeepCPI algorithm for drug-target interaction (DTI) prediction.…”

Artificial Intelligence in Translational Medicine

“…Furthermore, in drug discovery field, advanced computational models, based on ML technology, hve demonstrated strong potential in selecting effective hit compounds [51][52][53][54][55][56][57][58]. Moreover, ML-based approaches represent a valuable resource also in drug repurposing field [59,60]. Interestingly, these approaches have provided potential drugs for treating CoVid-19 in a short time [61,62].…”