Supervised machine learning in drug discovery and development: Algorithms, applications, challenges, and prospects

Drug discovery and development is a time-consuming process that involves identifying, designing, and testing new drugs to address critical medical needs. In recent years, machine learning (ML) has played a vital role in technological advancements and has shown promising results in various drug discovery and development stages. ML can be categorized into supervised, unsupervised, semi-supervised, and reinforcement learning. Supervised learning is the most used category, helping organizations solve several real-world problems. This study presents a comprehensive survey of supervised learning algorithms in drug design and development, focusing on their learning process and succinct mathematical formulations, which are lacking in the literature. Additionally, the study discusses widely encountered challenges in applying supervised learning for drug discovery and potential solutions. This study will be beneficial to researchers and practitioners in the pharmaceutical industry as it provides a simplified yet comprehensive review of the main concepts, algorithms, challenges, and prospects in supervised learning. • Conducted a survey of supervised learning algorithms in drug design and development. • Provided mathematical formulations of these algorithms, addressing a gap in literature. • Identified challenges in applying supervised learning to drug discovery with solutions. • Presented algorithm concepts, challenges, and prospects, aiding researchers and practitioners.