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
Drug discovery is undergoing a paradigm shift with the integration of multimodal AI, which combines diverse data types such as protein structures, genomic data, chemical properties, and clinical insights. Breakthroughs like AlphaFold have revolutionized protein structure prediction, enabling researchers to understand molecular interactions with unprecedented accuracy. However, the next frontier lies in integrating these structural insights with generative AI models to design novel therapeutics efficiently. Across 4 Weeks, you will go deep on protein structures, genomic data, and chemical properties, 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
Drug discovery is undergoing a paradigm shift with the integration of multimodal AI, which combines diverse data types such as protein structures, genomic data, chemical properties, and clinical insights. Breakthroughs like AlphaFold have revolutionized protein structure prediction, enabling researchers to understand molecular interactions with unprecedented accuracy. However, the next frontier lies in integrating these structural insights with generative AI models to design novel therapeutics efficiently.
📋 Course Objectives
1. Get comfortable working with protein structures.
2. Build practical fluency in genomic data.
3. Gain working command of chemical properties.
4. Translate biotechnology theory into practical, reproducible analysis.
5. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 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 protein structures
🚀 Key Learning Outcomes
• Confidence to reason about protein structures in real projects.
• Confidence to apply genomic data in real projects.
• Confidence to implement chemical properties in real projects.
• Tangible, reproducible biotechnology work to show supervisors or employers.
• 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
Predicted Structures as a Starting Point
• AlphaFold and the AlphaFold Database: what is available without running anything
• pLDDT and PAE together, and why a low-confidence loop is often genuine disorder
• AlphaFold-Multimer for complexes and the sharply lower reliability of interfaces
Representing Different Data Types
• Sequence, graph, structure and text encoders and what each captures
• Transcriptomic and clinical data as additional evidence about a target
• Fusion strategies — early, late and cross-attention — and their failure modes
Designing Molecules with Models
• SMILES, graph and 3D diffusion generators compared on validity and novelty
• Structure-conditioned generation and the tendency to produce unsynthesisable output
• Synthetic accessibility scoring and retrosynthesis checks as a required gate
Generative Design of Binders
• RFdiffusion and ProteinMPNN in the design-then-sequence workflow
• In silico filtering of designs before any wet-lab commitment
• Reported success rates and why most designed binders still fail
Reading Claims Critically
• Benchmark leakage and why held-out targets matter more than headline metrics
• Where multimodal models genuinely beat single-modality baselines, and where they do not
• Translating a computational hit into a testable experimental plan
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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