An Overview of Computational‐Based Strategies for Drug Repositioning

Drug repositioning is a technique for discovering new uses for investigational and approved drugs that are beyond the scope of the original medication indication. It enhances the drug's therapeutic value, which raises the success rate. There are different approaches which offer several advantages over developing a novel drug for a given indication. In this study, we have discussed various strategies for drug repositioning like the use of the internet of things (IoT), artificial intelligence (AI), machine learning (ML), cloud computing, big data digital twin, etc. AI and ML allow for the rapid development of drugs, including the repositioning of existing drugs. IoT has penetrated the majority of application domains, the most important being in the field of healthcare. The integration of drug repositioning with the internet of things (IoT), artificial intelligence (AI), machine learning (ML), cloud computing, big data and digital twining, further enriches its significance and research potential with novel opportunities and unique challenges. The objective of this review is to demonstrate the value of these computational tools for association prediction and drug repositioning in novel disease indications. A huge volume of biological and biomedical data is being produced right now as a result of the quick development of high-performance technology. This enables the creation of new opportunities for drug repositioning through the application of computational techniques and models based on biological networks.