Advancing Pharmaceuticals with Machine Learning: A Short Review of Research and Development Applications

Abstract

In recent years, the field of pharmaceutical research and development (RD) has seen a surge of interest in artificial intelligence (AI) and machine learning (ML) technologies. These advancements may transform the industry by addressing challenges related to data analysis, computational capabilities, and rising costs associated with drug development. ML techniques have been progressively refined and applied to various stages of drug discovery over the past 15–20 years. Notably, there is a growing focus on utilizing ML in clinical trial design, conduct, and analysis, which the COVID-19 pandemic and the increased reliance on digital technology in clinical trials have further accentuated. However, it is crucial to move beyond mere buzzwords and acknowledge that the scientific method remains essential for drawing meaningful insights from data. By doing so, we can distinguish between genuine advancements and exaggerated claims, leading to informed decision-making regarding the optimal integration of ML methods in drug development. This review aims to provide a comprehensive understanding of key concepts, understand real-world usage, and offer a balanced perspective on the effective utilization of ML in RD.