AI Applications in Pharmaceutical Sciences (Theory)

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.

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

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

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) 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.

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