BP604T · Core · Semester 6
AI Applications in Pharmaceutical Sciences
Explore how pharmaceutical data can be structured and discussed across natural products, medicinal chemistry, formulation, manufacturing, and analytical science through transparent fictional educational simulations.
Course objectives
- Introduce AI and machine-learning applications across major pharmaceutical disciplines.
- Understand structured data in natural products, medicinal chemistry, formulation science, and manufacturing.
- Interpret regression and classification concepts in bounded drug-discovery, product-performance, and quality-monitoring teaching cases.
- Build awareness of predictive-modeling limits in pharmaceutical decision-making.
- Understand multivariate analysis concepts in pharmaceutical analytical techniques.
Unit 1: AI in Natural Products & Pharmacognostic Data Modeling
Natural-product AI begins by structuring crude-drug morphological, microscopic, phytochemical, and chromatographic features. A fictional classification or regression display can teach authenticity, yield, screening, or herb-drug-interaction vocabulary, but cannot authenticate a crude drug, establish efficacy or safety, or make a discovery decision.
Adjust fictional natural-product features, identify a data source and model type, and explain why the educational score cannot authenticate a material or predict safety.
Unit 2: AI in Medicinal & Pharmaceutical Chemistry
Molecular structures can be represented by numerical descriptors such as MW, logP, HBD/HBA, and TPSA. Structured chemical datasets, QSAR, virtual screening, lead-optimization, activity, solubility, ADMET, and toxicity vocabulary can be taught transparently; a descriptor heuristic is not a validated drug-discovery prediction.
Change an illustrative descriptor profile, distinguish a structured dataset from a prediction, and name a chemical-reasoning limitation.
Unit 3: AI in Drug Product Design & Performance Prediction
Formulation development balances dosage-form variables, excipient selection, solubility, bioavailability, drug release, stability trends, degradation, and shelf-life concepts. A transparent score makes a fictional trade-off visible, while real product decisions require experiments, specifications, stability evidence, and pharmaceutical judgement.
Explore fictional formulation trade-offs for solubility, release, or stability and state which experimental evidence is still needed.
Unit 4: AI in Process Monitoring & Production Systems
Digital manufacturing data can describe CQAs, CPPs, process variability, correlation, logistic-regression pass/fail concepts, feature importance, and predictive maintenance. The teaching model does not define a CQA, CPP, alert limit, batch disposition, manufacturing-release decision, or regulatory conclusion.
Set a fictional observation outside the teaching band, relate it to CQA/CPP vocabulary, and explain why validation and human investigation remain necessary.
Unit 5: AI in Pharmaceutical Analysis & Chemometrics
Chemometrics and multivariate analytical data help discuss UV/IR, chromatographic peak, quantitative regression, genuine/counterfeit classification, noise, and variability concepts. A browser similarity display cannot identify a compound, validate an analytical method, or make an analytical, quality, or regulatory decision.
Adjust fictional analytical inputs, distinguish regression and classification language, and state why noise and validation evidence limit interpretation.