PHM5013 – Precision Drug Discovery and Pharmacogenomics
Course Overview
This course provides an integrated understanding of modern drug discovery within the framework of precision medicine. It bridges computational, genomic, and regulatory sciences to demonstrate how drug response and safety are influenced by molecular structure, genetic variation, and population diversity. Students will learn key computational approaches—such as virtual screening, QSAR modelling, molecular dynamics, and AI-based prediction—alongside pharmacogenomic principles that guide individualized therapy. The course further explores regulatory pathways and pharmacovigilance through case studies from the Health Sciences Authority (HSA) and international agencies, highlighting the translation of genomic data into clinical and policy applications.
Learning Outcomes
By the end of this module, students will be able to:
1. Explain the principles of modern drug discovery and pharmacogenomics.
2. Apply computational and AI tools to optimize molecular drug design.
3. Interpret genetic and population-based determinants of variability in drug efficacy and safety.
4. Evaluate regulatory frameworks and real-world case studies of pharmacogenomics-driven pharmacovigilance
Course Outline
1. Pharmaceutical Development Pipeline: An integrated overview of the drug discovery and development continuum, spanning target identification and validation, lead optimization, non-clinical development, clinical trial design across Phases I to IV, and regulatory approval.
2. Target Identification and Computer-driven Drug Discovery: Ligand-based virtual screening and quantitative structure-activity relationship (QSAR) modelling, covering chemical representations (pharmacophores, fingerprints, molecular descriptors, graph embeddings), molecular similarity and scaffold analysis, library curation principles (Lipinski’s Rule of Five, PAINS, ADMET), and machine learning approaches ranging from autoencoders to graph convolutional networks, transformers, and message-passing neural networks.
3. Structural Biology and Computational Modelling in Drug Design: Biomolecular structure prediction through physics-based methods (homology modelling, threading, ab initio) and AI-based approaches (AlphaFold2), coupled with structure-based virtual screening (docking search algorithms, force-field, empirical, knowledge-based and ML-based scoring functions, diffusion models, GNNs) and Molecular Dynamics simulations with enhanced sampling techniques (replica exchange, metadynamics, umbrella sampling).
4. Translational and Clinical Pharmacogenomics: Integration of genetic and non-genetic pharmacokinetic and pharmacodynamic variability into drug development and clinical practice, covering drug transporters, metabolizing enzymes, drug targets, pre-emptive and population pharmacogenomics, pharmacovigilance, clinical implementation, and pharmaco-economic frameworks.
Course Requirements
Students must have taken the core course PHM5001 Human Genomics in Precision Medicine and PHM5005 AI and Machine Learning in Precision Medicine.