Published Paper Details:

The pharmaceutical industry continues to face significant challenges in re-ducing costs and timelines associated with drug discovery and develop-ment. Artificial intelligence (AI), particularly machine learning (ML) appli-cations in molecular modeling, has emerged as a transformative asset. This white paper reviews the technological advancements and challenges in ap-plying AI to drug discovery over the past five years (2018-2023), empha-sizing FDA-approved drug development 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 inves-tigate 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 ma-chine learning concepts, aiming to bridge the gap between research and prac-tical application