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.in/syllabus/semester-3/bp301t-introduction-to-machine-learning-in-pharmaceutical-sciences/

Unit-wise Syllabus

5 Units

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

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

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

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

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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