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AKTU AI applications in Pharmaceutical Sciences (Theory) (BP604T) Syllabus PDF – B.Pharm 6th Semester
AI applications in Pharmaceutical Sciences (Theory) BP604T
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Official AKTU syllabus of AI applications in Pharmaceutical Sciences (Theory) (BP604T) for B.Pharm 6th Semester – 2 credits, L-T-P 2-0-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — AI applications in Pharmaceutical Sciences (Theory)
Download PDFBP604T · 2 credits · L-T-P 2-0-0
Course Objectives
- Introduce applications of artificial intelligence and machine learning across major pharmaceutical disciplines.
- Understand the structured data in natural products, medicinal chemistry, formulation science, and manufacturing
- Interpret regression and classification models in drug discovery, product performance, and quality monitoring.
- Build awareness of predictive modeling for pharmaceutical decision-making.
- Understand multivariate analysis in pharmaceutical analytical techniques.
Course Outcomes
| CO | Upon successful completion of this course, the students will be able to: |
| No. | |
| 1 | Explain how pharmaceutical data from various domains can be structured for machine learning applications. |
| 2 | Interpret regression and classification models used in natural products and medicinal chemistry. |
| 3 | Analyze predictive modeling approaches in formulation, release, and stability studies. |
| 4 | Evaluate AI applications in manufacturing process monitoring and quality prediction. |
| 5 | Assess analytical modeling and chemometric applications in pharmaceutical quality control. |
Course Content
| Unit | Topics | No. of |
|---|---|---|
| I | AI in Natural Products & Pharmacognostic Data Modeling Overview of ML applications in natural products: Representation of crude drug data (morphological, microscopic, phytochemical, chromatographic features) Structuring herbal datasets for predictive modeling 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. | 6 Hours |
| II | 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 Virtual screening and lead optimization concepts Limitations of ML models and importance of chemical reasoning | 6 Hours |
| III | 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 | (6 Hours) |
| IV | 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 Parameters (CPPs) | 6 hours |
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.
| V | 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 | 6 Hours |
Recommended References
- Bakeev, K. A. Process Analytical Technology: Spectroscopic Tools and Implementation Strategies for the Chemical and Pharmaceutical Industries. Wiley.
- Brown, N. Artificial Intelligence in Drug Discovery. Royal Society of Chemistry.
- Chavda, V., et al. Bioinformatics Tools for Pharmaceutical Drug Product Development.
- Gibson, M. Pharmaceutical Preformulation and Formulation. CRC Press.
- Hanessian, S. Natural Products in Medicinal Chemistry. Wiley.
- Pais, A., et al. Artificial Intelligence for Drug Product Lifecycle Applications.
- Schlindwein, W. S. and Gibson, M. Pharmaceutical Quality by Design: A Practical Approach. Wiley.
- World Health Organization. Quality Assurance of Pharmaceuticals: A Compendium of Guidelines and Related Materials. WHO Press
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