Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{174571,
        author = {Ms. Nandinee P. Lavhe and Ms. Sayali D. Zinge and Ms. Shraddha G. Shirsat and Ms. Bhakti D. Phand},
        title = {The Role Of AI In Drug Discovery: Accelerating Pharmacological Innovations},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {16-20},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174571},
        abstract = {The integration of Artificial Intelligence (AI) in drug discovery is revolutionizing the pharmaceutical industry by accelerating the identification, development, and optimization of novel therapeutics. Traditional drug discovery methods are time-consuming, costly, and often suffer from high failure rates. AI-driven approaches, including machine learning (ML), deep learning (DL), and natural language processing (NLP), enable rapid analysis of complex biological datasets, prediction of molecular interactions, and identification of potential drug candidates with enhanced precision.AI-powered computational models streamline hit identification, lead optimization, and biomarker discovery by leveraging vast biochemical and genomic data repositories. Advanced algorithms facilitate virtual screening, structure-based drug design, and de novo drug synthesis, significantly reducing experimental costs and development timelines. Additionally, AI-driven pharmacovigilance systems enhance drug safety monitoring by detecting adverse drug reactions (ADRs) through real-world evidence and electronic health records (EHRs).Despite its transformative potential, AI-driven drug discovery faces challenges such as data quality, model interpretability, regulatory compliance, and ethical considerations. Addressing these hurdles requires interdisciplinary collaboration between pharmaceutical scientists, bioinformaticians, and regulatory bodies. The future of AI in drug discovery lies in the integration of quantum computing, federated learning, and multi-omics data to achieve precision medicine breakthroughs.This paper explores AI's role in drug discovery, highlighting its impact on target identification, drug repurposing, and clinical trial optimization. By harnessing AI's predictive capabilities, the pharmaceutical industry can accelerate pharmacological innovations, ultimately improving patient outcomes and reducing healthcare burdens.},
        keywords = {AI In Drug Discovery, Pharmacological Innovations.},
        month = {March},
        }