M.Sc. AI in Pharmaceuticals
This Course Structure is Curated as per the NEP-2020 Guidelines
Course Overview
The Master of Science (M.Sc.) in AI in Pharmaceuticals is an innovative and interdisciplinary postgraduate program designed to integrate the evolving field of artificial intelligence (AI) with the pharmaceutical sciences. This program equips students with the knowledge and skills necessary to apply AI and machine learning (ML) techniques across the drug discovery, development, clinical trials, regulatory, and marketing domains of the pharmaceutical industry.
Course Details
Description: 2 Years Degree Program
No. of Seats: 20
No. of Credits: 80 minimum & as specified
Bachelor’s degree in Pharmacy, Life Sciences, Biotechnology, Computer Science, Bioinformatics, or related disciplines with a minimum qualifying score as per institutional norms.
| Semester | Courses |
|---|---|
| I Year – I Semester | Introduction to AI & Machine Learning in Pharma Pharmaceutical Sciences & Drug Development Bioinformatics & Computational Biology Pharmacovigilance & AI in Drug Safety Monitoring Regulatory & Ethical Considerations in AI-Driven Pharma Open Electives – I: 1. Computational Chemistry 2. Digital Twins in Pharma 3. Pharmaceutical Data Science & Big Data Analytics Practical: AI & Data Science Lab Yogic Science & Wellbeing |
| I Year – II Semester | AI for Drug Discovery & Development Intellectual Property Rights (IPR) Natural Language Processing (NLP) for Pharma Research AI in Pharmaceutical Manufacturing & Quality Control Network Pharmacology & Chemoinformatics Open Electives – II: 1. Blockchain & AI for Pharma Supply Chain 2. AI in Target Identification & Biomarker Discovery 3. Cloud Computing & AI Deployment in Pharma Practical: AI for Drug Discovery Lab Journal Club, Assignment and Presentation (Industrial Case Studies in AI) |
| II Year – I Semester | AI Tool Application in Dosage Form Designs AI for Advanced Drug Delivery System (Solid & Liquid) AI in Pharmacovigilance AI in Pharmaceutical Manufacturing & Supply Chain Research Methodology & Biostatistics Elective – III: 1. AI for Pharma Entrepreneurship & Innovation 2. AI in Personalized Medicine & Digital Therapeutics 3. AI-Driven Medical Image Analysis Practical: AI Tools in Pharma Lab |
| II Year – II Semester | AI for Pharmacokinetics and Pharmacodynamics AI in Drug Repurposing & Rare Disease Research *Industry Internship in AI-Driven Pharma Companies Thesis/Research Project/Dissertation Report Evaluation (External) *Internal Assessment Final Dissertation Presentation & Viva |
PO | Program Outcomes |
PO-1 | Advanced Knowledge of AI & Pharmaceutical Sciences: Demonstrate comprehensive understanding of artificial intelligence, machine learning, and data analytics as applied to pharmaceutical sciences, drug discovery, and healthcare |
PO-2 | Problem Solving & Critical Thinking: Apply computational tools and AI-based models to analyze, simulate, and solve complex problems in pharmaceutical research and development. |
PO-3 | Data-Driven Decision Making: Develop the ability to collect, interpret, and derive insights from large and diverse biomedical and pharmaceutical datasets using AI and machine learning techniques |
PO-4 | Research & Innovation:Conduct independent research that integrates AI with pharmaceutical science to design innovative solutions for drug discovery, formulation, diagnostics, and personalized medicine. |
PO-5 | Ethics & Regulatory Understanding: Exhibit awareness of ethical, legal, and regulatory standards related to AI in healthcare and pharmaceuticals, ensuring responsible use of technology. |
PO-5 | System Development & Deployment:Design, implement, and evaluate AI systems for pharmaceutical applications, ensuring reliability, scalability, and compliance with industry standards. |
PO-6 | Innovation and Entrepreneurship:Identify opportunities and create AI-enabled products, services, or startups in pharmaceutical and healthcare domains. |
PO-7 | Communication & Collaboration: Communicate effectively in professional and interdisciplinary teams, presenting complex AI-based pharmaceutical concepts clearly to both technical and non-technical stakeholders. |
PO-8 | Leadership and Collaboration: Collaborate effectively within interdisciplinary teams and lead AI-driven pharmaceutical projects with strategic vision and innovation. |
PO-9 | Lifelong Learning and Technological Adaptability: Engage in continuous learning to stay abreast of emerging technologies, methodologies, and trends in AI and pharmaceutical sciences. |
PO-10 | Societal and Global Impact:Assess and address the societal, environmental, and global implications of AI in healthcare, ensuring inclusivity and sustainability. |
Career Enhancement through M.Sc. in Artificial Intelligence in Pharmaceuticals
The M.Sc. in AI in Pharmaceuticals program is designed not only to provide academic knowledge but also to strategically enhance students’ career potential in both the pharmaceutical and technology sectors. Here’s how the program supports career development:
- Industry-Relevant Skill Development
- AI/ML Expertise: Hands-on training in Python, TensorFlow, PyTorch, and R for AI-based pharmaceutical solutions.
- Pharma Domain Proficiency: In-depth understanding of pharmacology, drug development, clinical trials, and regulatory science.
- Data Science Integration: Training in big data analytics, bioinformatics, cheminformatics, and real-world data interpretation.
- Certifications and Workshops
- Industry-recognized certifications in AI, Machine Learning, and Pharmacovigilance.
- Workshops on emerging technologies (e.g., NLP in drug repurposing, blockchain in clinical data security).
- Internships & Live Projects
- Collaborations with pharmaceutical companies, AI startups, CROs (Contract Research Organizations), and healthcare analytics firms.
- Semester-long capstone project or thesis aligned with industry use-cases.
- Research and Publication Opportunities
- Guided research projects in computational pharmacology, digital therapeutics, and AI-led diagnostics.
- Encouragement to publish in indexed journals or present at conferences (e.g., DIA, ISPOR, AICHE).
- Career Pathways
Graduates are equipped to take up roles such as:
- AI Scientist in Drug Discovery
- Clinical Data Scientist
- Bioinformatics Analyst
- Machine Learning Engineer in Pharma
- Healthcare Data Strategist
- Pharmacovigilance AI Analyst
- Regulatory Tech Consultant
- R&D Informatics Manager
- Higher Education & Global Opportunities
- Eligible for doctoral programs (Ph.D.) in Pharmaceutical Sciences, AI in Healthcare, or Biomedical Informatics.
- Competitive for international fellowships and research positions across Europe, US, and Asia-Pacific.
- Entrepreneurial Support
- Incubation and mentorship for students with startup ideas in pharma-tech or digital health.
- Guidance on IP, patent filing, and funding for AI-driven healthcare innovations.
Higher Studies Opportunities after M.Sc. in Artificial Intelligence in Pharmaceuticals
Graduates of the M.Sc. in AI in Pharmaceuticals program are uniquely positioned to pursue advanced academic and research opportunities both in India and abroad. The interdisciplinary nature of the degree opens up a wide range of higher studies pathways across domains such as pharmaceutical sciences, biomedical informatics, artificial intelligence, and computational biology.
- Doctoral Programs (Ph.D.)
- In India:
- Ph.D. in Pharmaceutical Sciences (with focus on AI applications)
- Ph.D. in Bioinformatics / Cheminformatics
- Ph.D. in Data Science or AI in Healthcare
Institutions: NIPERs, IITs (AI/BSBE), IIIT Hyderabad, IISc, BITS Pilani, etc.
- Abroad:
- Ph.D. in Biomedical Informatics / Computational Biology / Health Data Science
Countries: USA, UK, Germany, Canada, Australia
Universities: MIT, Stanford, ETH Zurich, University of Oxford, University of Toronto, etc.
- Specialized Master’s & Certifications Abroad
Graduates may also pursue niche, high-impact postgraduate programs such as:
- M.S. in Health Informatics / Biomedical Data Science
- M.S. in Regulatory Science and Digital Health
- Postgraduate Diploma in Clinical AI / Digital Pharmacology
- MRes in Translational Medicine & AI Applications
- Research Fellowships & International Grants
- Marie Skłodowska-Curie Actions (EU)
- Fulbright-Nehru Doctoral Fellowships
- DAAD (Germany), Commonwealth Scholarships (UK), Erasmus+
- CSIR / UGC-JRF (India) for Ph.D. entrance with stipend support
- Interdisciplinary Pathways
With this degree, students can also transition into:
- Ph.D. in AI and Ethics in Healthcare
- Doctoral studies in Public Health Analytics
- Dual Ph.D. programs (e.g., AI + Pharmacogenomics)
- Teaching & Academic Careers
Graduates can enter academia as:
- Assistant Professors in pharmacy, data science, or AI in healthcare.
Academic Researchers with institutions focusing on translational AI, pharmacometrics, and precision medicine.
Graduates of the M.Sc. in AI in Pharmaceuticals are equipped with both domain-specific knowledge and advanced AI competencies, opening a diverse range of job roles across pharmaceutical, biotech, healthcare, and data science sectors. Below is a structured view of potential career tracks and progression:
Entry-Level Job Roles (0–2 Years Experience)
Job Role | Key Responsibilities |
Clinical Data Analyst | Analyze clinical trial data using AI tools; ensure regulatory compliance. |
Pharma Data Scientist | Build models for drug efficacy prediction, dosage optimization, etc. |
AI Research Assistant (Pharma) | Support R&D teams in AI-led drug discovery and diagnostics. |
Bioinformatics Analyst | Perform genomic/proteomic data analysis using ML algorithms. |
Pharmacovigilance Analyst (AI-supported) | Use NLP and ML to detect adverse drug reactions and safety signals. |
Regulatory Tech Associate | Automate documentation and submission workflows using AI tools. |
Health Informatics Specialist | Develop intelligent systems to interpret patient data in clinical settings. |
Mid-Level Roles (2–5 Years Experience)
Job Role | Career Focus |
Machine Learning Engineer (Healthcare) | Design and deploy scalable ML models for diagnostics and drug discovery. |
AI Product Manager – Pharma/HealthTech | Lead development of AI tools in pharma companies or startups. |
Lead Clinical AI Analyst | Manage large datasets, build predictive models for clinical decision-making. |
R&D Informatics Scientist | Integrate AI with wet-lab and dry-lab research in pharmaceutical pipelines. |
Computational Pharmacologist | Apply simulation and modeling in pharmacokinetics/dynamics using AI. |
Senior-Level & Strategic Roles (5+ Years Experience)
Job Role | Scope of Work |
Director – Digital Drug Discovery | Oversee AI integration across preclinical and clinical R&D operations. |
Chief Data Scientist – Pharma | Strategize enterprise-wide data and AI initiatives. |
Head – AI & Innovation Lab (Healthcare) | Lead interdisciplinary teams solving grand challenges in medicine using AI. |
Entrepreneur / Founder – PharmaTechStartup | Build AI-powered platforms or services in drug development, diagnostics, or personalized medicine. |
Note: Salaries vary based on experience, location, and type of healthcare institution.
Fee Structure Per Academic Year
| Tuition Fee | Miscellaneous Fee | Scholarship | ||
| 175000 ₹ | 10000 ₹ | Above 90% – 35000 ₹ | Between 81-90% – 17500 ₹ | Between 71-80% – 8750 ₹ |
