Level: Bachelor of Pharmacy
Overview: Explores the chemical processes and molecular architectures within living organisms. Key topics include biomolecular structure and function, enzyme kinetics, metabolic pathways, energy transformation, and signal transduction pathways critical to drug action.
Format: Lectures, Interactive Workshops, and Laboratory Practicums.
Level: Bachelor of Pharmacy (S1)
Overview: Focuses on the pharmacological principles governing anti-infective therapy. Covers mechanisms of antimicrobial action, mechanisms of drug resistance, PK/PD targets of antibiotics, antifungal and antiviral pharmacotherapy, and clinical strategies to combat global antimicrobial resistance (AMR).
Format: Lectures, Clinical Case Discussions, and Interactive Seminars.
Level: Bachelor of Pharmacy (S1)
Overview: An introduction to the application of computer science, information management, and computational tools in pharmaceutical sciences. Covers chemical structure databases, biological data retrieval, cheminformatics, including molecular visualization
Format: Lectures and Hands-on Computational Labs.
Level: Master of Pharmacy (S2)
Overview: Advanced study in modern computational methodologies for rational drug discovery. Topics include ligand-based and structure-based drug design, virtual screening protocols, molecular docking, molecular dynamics simulations, and ADMET property prediction.
Format: Lectures, Case Studies, and Project-based Computational Workshops.
Level: Master of Pharmacy (S2)
Overview: In-depth investigation of drug-target interactions at the macromolecular level. Focuses on receptor theory, signal transduction pathways, target identification and validation, and molecular mechanisms of antimicrobial and neuroprotective drug action.
Format: Advanced Seminars and Journal Club Article Discussions.
Level: Master of Biotechnology (S2)
Overview: Focuses on computational techniques for analyzing biological datasets. Covers sequence alignment, phylogenetic analysis, genomic data mining, protein structural analysis, and biological network pathways relevant to modern biotechnology.
Format: Lectures and Applied Computational Assignments.
Level: Master of Biotechnology (S2)
Overview: Explores the applications of machine learning, deep learning, and predictive modeling in biological research and drug discovery. Key areas include predictive toxicity models, protein structure prediction, target identification algorithms, and generative chemistry frameworks.
Format: Project-Oriented Seminars and Practical Coding Tutorials.