Artificial Intelligence and Pharmaceutical Discovery: A Comprehensive Review of Methods, Applications, and Future Directions

Senguttuvan N, Prabhakar K, Christopher A, Parida P

Abstract: Artificial Intelligence (AI) is rapidly transforming the landscape of drug discovery, offering a powerful toolkit for accelerating and enhancing the entire pharmaceutical pipeline from molecular design and target identification to clinical trial optimization. Traditional drug development is costly, time-consuming, and prone to high failure rates, but AI-driven methods are helping to mitigate these challenges by enabling high-throughput virtual screening, predictive modeling of protein-ligand interactions, de novo molecule generation, and personalized therapeutic strategies. Recent breakthroughs such as the application of deep generative models, graph neural networks, and natural language processing have demonstrated success in identifying novel compounds, repurposing existing drugs, and uncovering complex biological relationships. This paper presents a comprehensive literature review of state-of-the-art AI methodologies applied to drug discovery, highlights key case studies, and evaluates their performance, limitations, and translational potential. We also discuss the ethical, regulatory, and interpretability challenges that must be addressed to ensure the responsible integration of AI into biomedical research. As the field moves toward a paradigm of data-driven, AI-augmented drug development, interdisciplinary collaboration and open innovation will be critical to realizing the full potential of these technologies in delivering safe and effective therapies.

Keywords: Artificial Intelligence, Drug Discovery, Deep Learning, Virtual Screening, Biomedical Innovation

How to Cite?: Senguttuvan N, Prabhakar K, Christopher A, Parida P, "Artificial Intelligence and Pharmaceutical Discovery: A Comprehensive Review of Methods, Applications, and Future Directions", Volume 14 Issue 8, August 2025, International Journal of Science and Research (IJSR), Pages: 679-681, https://www.ijsr.net/getabstract.php?paperid=SR25808215949, DOI: https://dx.doi.org/10.21275/SR25808215949