📚 Syllabus & Course Curriculum
Drug Discovery & Pharmaceutical Sciences
Module-by-module breakdown of End-to-End AI for Drug Discovery, Delivery, and Biological Validation, from foundations to a certified capstone project.
Target
• Target identification from Open Targets and genetic evidence for tractability
• Genetic support roughly doubles clinical success — how to use that in practice
• Distinguishing a druggable target from a merely disease-associated one
Lead
• Virtual screening and property prediction placed in the discovery timeline
• Multi-parameter optimisation: potency, selectivity and ADMET traded off together
• Why single-objective optimisation reliably produces undevelopable compounds
Delivery
• Solubility, permeability and BCS classification driving formulation choice
• Nanoparticle and lipid-nanoparticle systems, and ML for formulation parameters
• Release profile modelling and the gap between in vitro and in vivo behaviour
Validation
• Assay design, controls and the statistical power an in vitro screen actually needs
• Cell line, organoid and animal models ranked by translational relevance
• Orthogonal assays as the standard defence against a target-engagement artefact
Programme
• Where AI genuinely compresses the timeline and where it does not
• Data capture and versioning so predictions can be audited later
• Go / no-go criteria set in advance rather than argued after the result
Earn government-registered certification in End-to-End AI for Drug Discovery, Delivery, and Biological Validation
e-Certificate and e-Marksheet issued on successful completion.
