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Semester VI · PCI syllabus 2026
AI Applications in Pharmaceutical Sciences
BP604T
2 credits
Theory
Coming soon
Unit 1: AI in Natural Products & Pharmacognostic Data Modeling
- Overview of ML applications in natural products:
- Representation of crude drug data (morphological, microscopic, phytochemical, chromatographic features)
- Classification models for authentication of crude drugs (authentic vs adulterated)
- Regression models for prediction of secondary metabolite yield
- Application of AI in phytochemical screening and prioritization
- Herb-drug interaction prediction using database-driven approaches
- Limitations of predictive modeling in natural products
Unit 2: AI in Medicinal & Pharmaceutical Chemistry
- Overview of the role of AI applications in pharmaceutical chemistry with focus on drug discovery concepts:
- Conversion of molecular structures into numerical descriptors (MW, logP, HBD/HBA, TPSA)
- Structured chemical datasets and QSAR modeling
- Regression and classification models for predicting biological activity (IC₅₀, solubility, ADMET properties), toxicities
- Virtual screening and lead optimization concepts
- Limitations of ML models and importance of chemical reasoning
Unit 3: AI in Drug Product Design & Performance Prediction
- Overview of AI models across formulation development stages, with emphasis on the applications and limitations:
- Overview of dosage form development and formulation variables
- Machine learning in formulation optimization and excipient selection
- Regression modeling for solubility and bioavailability enhancement
- Time-concentration modeling for drug release interpretation
- Stability study design and modeling degradation trends
- Shelf-life estimation using regression-based approaches
- Practical limitations of extrapolation in formulation science
Unit 4: AI in Process Monitoring & Production Systems
- Overview of introduces multivariate analysis in pharmaceutical analytical techniques with focus on the different models and their applications:
- Digital data acquisition in pharmaceutical production
- Critical Quality Attributes (CQAs) and Critical Process
- Correlation analysis for identifying process variability
- Logistic regression models for batch pass/fail prediction
- Feature importance in process monitoring, predictive maintenance concepts. Ethical and regulatory considerations in automated decision- making, and limitations of predictive models in manufacturing environments
Unit 5: AI in Pharmaceutical Analysis & Chemometrics
- Overview of multivariate analysis in pharmaceutical analytical techniques with focus on the different models and their applications:
- Introduction to chemometrics and multivariate analytical data
- Spectroscopic data modeling (UV/IR interpretation)
- Regression analysis in quantitative pharmaceutical analysis
- Chromatographic peak modeling and interpretation
- Classification models for genuine vs counterfeit detection
- Handling noise and variability in analytical datasets
- Limitations of AI models in analytical decision-making
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