Drug Repurposing Based on Machine Learning
Laxmi Tripathi, Praveen Kumar, Kalpana Swain, Satyanarayan Pattnaik · 2022
Drug repurposing identifies new pharmacological indications for existing drugs. It is a promising approach due to the possibility of reduced development timelines and overall costs. The growth of substantial and publicly available electronic health-related and biomedical data leads the path of computer aided drug repurposing strategies. They repurpose drugs via drug-based strategies and disease-based strategies. Machine learning is a state-of-the-art screening approach. Databases widely used for drug repurposing included chemical, medical, pharmacological, and biological databases. This chapter compiles various data resources mentioned under various heads. The limitations of classical statistical approaches proved them ineffectual in drug repurposing and lead to inconsistent findings. Drug repurposing through machine learning algorithms could overcome the limitations of conventional statistical approaches and their unreliable interpretations. Further, case studies of drug examples repurposed through machine learning programs are also discussed in this chapter.