Programme Parameters
Foundation
3 Days (4.5 Hrs)
Online (e-LMS)
• No prior experience required — basic computer literacy is sufficient.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
This course introduces an end-to-end AI workflow for therapeutic innovation, beginning with target and compound selection, moving through drug delivery and formulation design, and ending with biological validation strategies. Across 4 Weeks, you will work hands-on with drug delivery and formulation design, then consolidate everything in a capstone project. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course introduces an end-to-end AI workflow for therapeutic innovation, beginning with target and compound selection, moving through drug delivery and formulation design, and ending with biological validation strategies.
📋 Course Objectives
1. Gain working command of drug delivery.
2. Develop hands-on skill in formulation design.
3. Apply biotechnology methods to authentic research and industry problems.
4. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
• Data and computational scientists moving into drug delivery
🚀 Key Learning Outcomes
• Confidence to apply drug delivery in real projects.
• Confidence to implement formulation design in real projects.
• A portfolio-grade biotechnology deliverable you can defend and extend.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Choosing What to Work On
• 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
From Hit to Candidate
• 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
Formulation and Delivery Design
• 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
Testing the Prediction
• 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
Running the Whole Workflow
• 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
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | AutoDock Vina |
| Covered Tool / Platform | PyRx |
| Covered Tool / Platform | Schrödinger Suite |
| Covered Tool / Platform | GROMACS |
| Covered Tool / Platform | ChemDraw |
| Covered Tool / Platform | Discovery Studio |
| Covered Tool / Platform | ADMET Predictor |
Frequently Asked Questions
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