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

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  • Md Abul Mansur

Abstract

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.

Suggested Citation

  • Md Abul Mansur, 2025. "Artificial Intelligence in Drug Discovery Towards Strategic Applications Challenges and Implementation Frameworks for Accelerated Pharmaceutical Innovation," European Journal of Artificial Intelligence and Machine Learning, European Open Science, vol. 4(3), pages 16-23, May.
    • Handle: RePEc:epw:ejai00:v:4:y:2025:i:3:id:1065
    DOI: 10.24018/ejai.2025.4.3.65
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