Artificial Intelligence in Drug Discovery Towards Strategic Applications Challenges and Implementation Frameworks for Accelerated Pharmaceutical Innovation

The drug discovery process is historically resource-intensive, time-consuming, and prone to high attrition rates. With the advancement of computational biology and data science, artificial intelligence (AI) has emerged as a transformative force capable of reshaping this landscape. This study explores the strategic integration of AI within early-stage drug discovery, focusing on its applications in target identification, compound generation, and drug-target interaction modeling. We benchmark a range of AI platforms and tools, evaluate their efficacy, and discuss infrastructural and regulatory challenges that inhibit broader implementation. Our analysis also presents a structured implementation roadmap for organizations aiming to adopt AI-driven workflows. The study concludes by highlighting the evolving trajectory of AI-enabled pharmaceutical research and offers practical recommendations for researchers, industry leaders, and policy stakeholders.