Advancements in AI for Drug Discovery: Exploring Machine Learning in Molecular Modeling (2018-2023)

The pharmaceutical industry continues to face significant challenges in reducing costs and timelines associated with drug discovery and development. Artificial intelligence (AI), particularly machine learning (ML) applications in molecular modeling, has emerged as a transformative asset. This white paper reviews the technological advancements and challenges in applying AI to drug discovery over the past five years (2018–2023), emphasizing FDA-approved drugdevelopment processes. This paper provides a comprehensive comparative analysis of AI models, detailed case studies from leading pharmaceutical companies, and a discussion on the regulatory frameworks and compliance standards governing AI in pharmaceutical research. In this study, we investigate how molecular modeling accuracy rates have improved and identify key implementation challenges, including data quality, interpretability of results, and integration into existing research workflows. The discussion is tailored for pharmaceutical researchers with an intermediate grasp of machine learning concepts, aiming to bridge the gap between research and practical application.