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Transforming Pharmaceutical Innovation Through Artificial Intelligence: Revolutionizing Drug Discovery


Authors : Ragni Patel; Dip Patel; Divya Patel; Dr. Manan Sharma; Dr. Anjali Gupta; Dr. Akshat Parashar

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/48cxsawp

Scribd : https://tinyurl.com/yvtcjxct

DOI : https://doi.org/10.38124/ijisrt/26apr430

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Abstract : Drug discovery still has a very high failure rate above 90% in clinical trials, takes 10 to 15 years to develop, and costs US$2․6 billion on average for each drug that finally reaches the market [1]․ These inefficiencies and the long time required for drug discovery processes present one of the major challenges to the provision of the required medicines․ However, the development of AI is transforming the drug discovery model into a faster, more precise and cheaper process․ AI is applied throughout the drug discovery process, screening the literature and high-throughput omics data (such as genomics, transcriptomics, proteomics and metabolomics), playing a key role in the accurate identification of novel disease targets through machine learning and natural language processing [2][5]․ The use of AI-based molecular docking and predictive models has allowed virtual screening to change from a time-consuming process to a faster high-throughput drug discovery process [6,17]․ AI-driven lead optimization generates and optimizes lead compounds using generative models such as GANs and reinforcement learning to provide the best combination of pharmacological and ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties [19,22,23]․ In addition to narrowing down patients for preclinical and clinical trials, Al can predict whether the trial will succeed or fail, and monitor its progress [7]․ By rapidly identifying adverse drug reactions from a variety of real-world data sources, Al systems significantly boost post-marketing surveillance [7]. Even though there are still issues with data quality, model interpretability, and the changing regulatory environment, Al's obvious advantages—such as significant time and cost savings in research, enhanced data integration, and the development of personalized medicine—highlight its crucial importance [4].Future drug research is anticipated to incorporate fully autonomous Al platforms and quantum computing, increasing treatment options, particularly for targets that were previously deemed "undruggable." Al is a significant change that will enable patients to receive life-saving drugs with previously unheard-of efficiency and speed. He is more than just a small improvement.

Keywords : Artificial Intelligence, Drug Discovery, Machine Learning, Clinical Trials.

References :

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Drug discovery still has a very high failure rate above 90% in clinical trials, takes 10 to 15 years to develop, and costs US$2․6 billion on average for each drug that finally reaches the market [1]․ These inefficiencies and the long time required for drug discovery processes present one of the major challenges to the provision of the required medicines․ However, the development of AI is transforming the drug discovery model into a faster, more precise and cheaper process․ AI is applied throughout the drug discovery process, screening the literature and high-throughput omics data (such as genomics, transcriptomics, proteomics and metabolomics), playing a key role in the accurate identification of novel disease targets through machine learning and natural language processing [2][5]․ The use of AI-based molecular docking and predictive models has allowed virtual screening to change from a time-consuming process to a faster high-throughput drug discovery process [6,17]․ AI-driven lead optimization generates and optimizes lead compounds using generative models such as GANs and reinforcement learning to provide the best combination of pharmacological and ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties [19,22,23]․ In addition to narrowing down patients for preclinical and clinical trials, Al can predict whether the trial will succeed or fail, and monitor its progress [7]․ By rapidly identifying adverse drug reactions from a variety of real-world data sources, Al systems significantly boost post-marketing surveillance [7]. Even though there are still issues with data quality, model interpretability, and the changing regulatory environment, Al's obvious advantages—such as significant time and cost savings in research, enhanced data integration, and the development of personalized medicine—highlight its crucial importance [4].Future drug research is anticipated to incorporate fully autonomous Al platforms and quantum computing, increasing treatment options, particularly for targets that were previously deemed "undruggable." Al is a significant change that will enable patients to receive life-saving drugs with previously unheard-of efficiency and speed. He is more than just a small improvement.

Keywords : Artificial Intelligence, Drug Discovery, Machine Learning, Clinical Trials.

Paper Submission Last Date
30 - April - 2026

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