BP301TSemester 32 creditsTheoryKEY SUBJECT
Introduction to Machine Learning in Pharmaceutical Sciences
Complete unit-wise syllabus for BP301T as per the PCI B.Pharm NEP 2020 curriculum (Semester 3 — Machine Learning + Pharmacology).
URL:
https://pharmacode.vercel.app/syllabus/semester-3/bp301t-introduction-to-machine-learning-in-pharmaceutical-sciences/Unit-wise Syllabus
5 Units1
Foundations of Machine Learning6 Hours- Definition and scope of Artificial Intelligence, Machine Learning, and Data Science; overview of ML workflow
- Types of ML: supervised, unsupervised, and reinforcement learning; key terminologies (features, labels, training, testing, validation)
- Data preprocessing for pharmaceutical data: handling missing values, encoding categorical variables, feature scaling, train-test split
2
Supervised Learning Algorithms6 Hours- Linear and logistic regression: concepts, applications in dose-response modelling and classification of drug activity
- Decision trees and Random forests: construction, overfitting, hyperparameter tuning
- k-Nearest Neighbours (kNN): algorithm, distance metrics, application in drug classification; model evaluation: accuracy, precision, recall, F1-score, ROC-AUC
3
Unsupervised Learning and Dimensionality Reduction6 Hours- Clustering: k-means algorithm, hierarchical clustering; applications in patient stratification and drug grouping
- Dimensionality reduction: Principal Component Analysis (PCA) — concept and pharmaceutical data applications
- Association rule mining: basics and applications in drug interaction detection
4
ML Applications in Pharmaceutical Sciences6 Hours- QSAR modelling: molecular descriptors, fingerprints; building QSAR models using regression and classification algorithms
- Virtual screening and drug discovery: activity prediction, ADMET property prediction
- Pharmacokinetics prediction using ML: Cmax, AUC, half-life prediction from molecular structure
5
Practical Implementation and Ethical Considerations6 Hours- Implementing ML models in Python using scikit-learn on pharmaceutical datasets (QSAR, ADR, clinical data)
- Model validation: cross-validation, confusion matrix, hyperparameter optimisation
- Ethical considerations in ML: bias in healthcare data, interpretability, responsible AI in pharmaceutical research
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Other subjects in Semester 3
BP302TEnvironmental SciencesBP303TEthics and Universal Human ValuesBP304TGeneral PharmacologyBP305THeterocyclic Compounds and StereochemistryBP306TPharmaceutical Dosage Forms IBP307TPharmaceutical EngineeringBP308TPharmaceutical MicrobiologyBP309PGeneral Pharmacology (Practical)BP310PHeterocyclic Compounds and Stereochemistry (Practical)BP311PPharmaceutical Dosage Forms I (Practical)BP312P AECAEC Elective — Nutraceuticals / Food Analysis / Yoga & Life Sciences (Practical)