BP604TSemester 62 creditsTheoryKEY SUBJECT

AI Applications in Pharmaceutical Sciences

Complete unit-wise syllabus for BP604T as per the PCI B.Pharm NEP 2020 curriculum (Semester 6 — AI in Pharma + Analysis + Internship II).

URL:https://pharmacode.in/syllabus/semester-6/bp604t-ai-applications-in-pharmaceutical-sciences/

Unit-wise Syllabus

5 Units

AI and ML in Drug Discovery and Natural Products6 Hours

Overview of AI/ML pipeline in drug discovery: target identification, hit generation, lead optimisation, ADMET prediction
ML applications in natural products: representation of crude drug data (morphological, microscopic, phytochemical, chromatographic features); classification models for botanical authentication
Deep learning for molecular property prediction: Graph Neural Networks (GNNs), transformer-based molecular models (BERT for chemistry); de novo drug design

QSAR and Molecular Descriptors6 Hours

Molecular descriptors: constitutional, topological, geometric, electronic descriptors; Morgan fingerprints, MACCS keys, ECFP
QSAR modelling: conversion of molecular structures to numerical descriptors; building predictive models for activity, toxicity, solubility, permeability
Structured chemical datasets; QSAR model validation; domain of applicability; QSAR software (RDKit, MOE, Discovery Studio)

AI in Pharmaceutical Formulation and Manufacturing6 Hours

Overview of dosage form development variables; ML in formulation optimisation: Design of Experiments (DoE) combined with ML, excipient compatibility prediction
Machine learning in manufacturing: real-time monitoring, predictive maintenance, process analytical technology (PAT) and ML integration; Industry 4.0 in pharma
AI in quality control: image analysis for tablet defects, NIR spectroscopy + ML for content uniformity, automated visual inspection systems

AI in Clinical and Analytical Pharmaceutical Sciences6 Hours

Multivariate analysis in pharmaceutical analytical techniques: PCA, PLS, cluster analysis applied to spectroscopic (UV, IR, NMR) and chromatographic data
AI in clinical trials: patient stratification, adaptive trial design, electronic patient-reported outcomes; natural language processing (NLP) in pharmacovigilance and literature mining
Computer-aided drug design (CADD): molecular docking (AutoDock, Glide, Vina); virtual screening; molecular dynamics simulations; AI-enhanced docking scoring functions

Chemometrics and Practical AI Implementation6 Hours

Introduction to chemometrics and multivariate analytical data: spectroscopic data modelling (UV/IR); regression analysis in quantitative pharmaceutical analysis
Practical AI tools: Python (scikit-learn, RDKit, DeepChem), KNIME, Jupyter notebooks; case studies — COVID-19 drug repurposing using ML, AI-based antibiotic discovery (Halicin)
Ethical, regulatory, and societal aspects of AI in pharmacy: bias in healthcare AI; FDA guidance on AI/ML-based software as a medical device (SaMD); responsible AI principles

Get complete notes for BP604T

Click any unit above to download its PDF notes — free, no login required