Authors :
Rajashree Somnath Chorgade; Srushti Dipak Nevase; Shubham Hanumant Dhavale; Dr. Santosh Waghmare
Volume/Issue :
Volume 11 - 2026, Issue 8 - August
Google Scholar :
https://tinyurl.com/2ezjp4pr
DOI :
https://doi.org/10.38124/ijisrt/26aug1574
Note : A published paper may take 4-5
working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and
ResearchGate.
Abstract :
Artificial Intelligence (AI) is rapidly transforming pharmaceutical research and drug development. The
traditional process of discovering and developing new medicines is time-consuming, expensive and associated with a high
rate of failure. AI, including machine learning, deep learning, natural language processing and generative AI, can analyses
large and complex datasets and assist researchers in making predictions and decisions. In preclinical research, AI can
support target identification, drug discovery, molecular screening, toxicity prediction, pharmacokinetic and
pharmacodynamics modelling, and analysis of animal and laboratory data. In clinical trials, AI can assist with protocol
design, patient recruitment, eligibility screening, clinical data management, monitoring, endpoint assessment, adverse-event
detection and prediction of trial outcomes. The U.S. Food and Drug Administration (FDA) reports increasing use of AI
across nonclinical, clinical, post-marketing and manufacturing stages of drug development. Despite these benefits, challenges
such as data quality, algorithmic bias, lack of transparency, privacy, cybersecurity, regulatory uncertainty and the need for
human oversight remain important. Therefore, appropriate validation, monitoring and risk-based regulatory approaches
are necessary for the safe and effective use of AI in pharmaceutical development.
Keywords :
Artificial Intelligence, Machine Learning, Preclinical Trials, Clinical Trials, Drug Development, Drug Discovery, Pharmacovigilance, Toxicity Prediction, Patient Recruitment. Generative AI.
References :
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- Artificial intelligence in drug development. Nature Medicine. 2024.
- U.S. Food and Drug Administration. Artificial Intelligence for Drug Development. FDA.
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- Integrating artificial intelligence in drug discovery and early drug development: a transformative approach. 2025.
Artificial Intelligence (AI) is rapidly transforming pharmaceutical research and drug development. The
traditional process of discovering and developing new medicines is time-consuming, expensive and associated with a high
rate of failure. AI, including machine learning, deep learning, natural language processing and generative AI, can analyses
large and complex datasets and assist researchers in making predictions and decisions. In preclinical research, AI can
support target identification, drug discovery, molecular screening, toxicity prediction, pharmacokinetic and
pharmacodynamics modelling, and analysis of animal and laboratory data. In clinical trials, AI can assist with protocol
design, patient recruitment, eligibility screening, clinical data management, monitoring, endpoint assessment, adverse-event
detection and prediction of trial outcomes. The U.S. Food and Drug Administration (FDA) reports increasing use of AI
across nonclinical, clinical, post-marketing and manufacturing stages of drug development. Despite these benefits, challenges
such as data quality, algorithmic bias, lack of transparency, privacy, cybersecurity, regulatory uncertainty and the need for
human oversight remain important. Therefore, appropriate validation, monitoring and risk-based regulatory approaches
are necessary for the safe and effective use of AI in pharmaceutical development.
Keywords :
Artificial Intelligence, Machine Learning, Preclinical Trials, Clinical Trials, Drug Development, Drug Discovery, Pharmacovigilance, Toxicity Prediction, Patient Recruitment. Generative AI.
