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

Obaido, George; Mienye, Ibomoiye Domor; Egbelowo, Oluwaseun F.; Emmanuel, Ikiomoye Douglas; Ogunleye, Adeola; Ogbuokiri, Blessing; Mienye, Pere; Aruleba, Kehinde

Abstract

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.

• Journal Article Type: Article
• Acceptance Date: Jul 13, 2024
• Online Publication Date: Jul 24, 2024
• Publication Date: Jul 24, 2024
• Deposit Date: Sep 30, 2024
• Publicly Available Date: Sep 30, 2024
• Journal: Machine Learning with Applications
• Peer Reviewed: Peer Reviewed
• Volume: 17
• Pages: 100576
• DOI: https://doi.org/10.1016/j.mlwa.2024.100576