Introduction:

Artificial Intelligence (AI) is transforming the pharmaceutical industry by accelerating drug discovery, improving success rates in clinical development, and reducing research and development costs. Traditional drug discovery is a time-consuming and expensive process that may take more than 10–15 years from target identification to market approval. AI technologies such as machine learning, deep learning, natural language processing, and predictive analytics are now being used to analyze large biological datasets, identify drug targets, design new molecules, predict drug–target interactions, optimize clinical trials, and enable personalized medicine. AI is therefore becoming an essential tool in modern pharmaceutical research, regulatory science, and industrial drug development.

The scope of AI in Drug Discovery & Development covers multiple stages of the drug lifecycle, including:

• Target identification and validation
• Biomarker discovery
• Lead identification and optimization
• Drug repurposing
• Preclinical data analysis and toxicity prediction
• Clinical trial design and patient recruitment
• Pharmacovigilance and adverse drug reaction prediction
• Regulatory decision support and real-world evidence analysis
• Personalized and precision medicine

AI is widely used in pharmaceutical companies, biotechnology companies, contract research organizations (CROs), regulatory agencies, and healthcare technology companies.

After completing this course, learners will be able to:

• Understand the role of AI and machine learning in drug discovery and development
• Explain how AI is used in target identification, lead optimization, and drug design
• Apply basic AI concepts to pharmaceutical data analysis
• Understand AI applications in clinical trials and pharmacovigilance
• Interpret AI-based predictive models used in pharmaceutical research
• Understand regulatory and ethical considerations of AI in healthcare
• Develop industry-relevant skills for careers in AI-based pharmaceutical research

This course is suitable for:

• B.Pharm students
• M.Pharm students
• Pharm.D students
• Life science students (Biotechnology, Microbiology, Biochemistry)
• Clinical research professionals
• Pharmacovigilance professionals
• Regulatory affairs professionals
• Data science beginners interested in healthcare
• Pharmaceutical industry professionals
• Researchers and academicians