End-to-End AI for Drug Discovery, Delivery, and Biological Validation
From Molecule to Mechanism—Build AI Workflows for Complete Therapeutic Development
About This Course
This workshop 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. Participants will explore how AI models assist in molecular screening, nanocarrier or delivery-system design, biomarker interpretation, and experimental result analysis. The focus is on dry-lab learning, translational thinking, and real-world application pathways relevant to biotech, pharma, and biomedical research.
Aim
This workshop aims to provide participants with a complete understanding of how AI can support the full therapeutic development pipeline—from drug discovery and delivery design to biological validation. It focuses on integrating molecular data, predictive modeling, formulation logic, and validation workflows into one end-to-end framework. Participants will learn how AI accelerates target selection, molecule screening, delivery optimization, and interpretation of biological outcomes. The program bridges computational discovery with translational bioscience.
Workshop Objectives
- Understand AI applications across discovery, delivery, and validation stages.
- Learn predictive workflows for target prioritization and compound screening.
- Explore drug delivery system design using data-driven and formulation logic.
- Analyze biological validation outputs for efficacy, toxicity, and biomarker response.
- Build an integrated end-to-end framework for therapeutic development.
Workshop Structure
Day 1: AI in Drug Discovery Foundations
- Target identification, hit discovery
- lead optimization, preclinical validation, Molecular descriptors
- QSAR basics, protein-ligand prediction, generative AI overview
- Hands-on: Molecular data handling, Basic QSAR model development, Compound property prediction
- Tools: Google Colab, Python, RDKit, Scikit-learn, Pandas, PubChem / ChEMBL datasets
Day 2: AI for Drug Delivery and Formulation Design
- Nanocarriers, liposomes, polymeric systems, release prediction & Feature selection
- formulation-performance mapping & predictive design
- Hands-on: Predict formulation performance from experimental variables, Visualize formulation-property relationships
- Tools: Python, Scikit-learn, XGBoost, Matplotlib, Jupyter Notebook
Day 3: Biological Validation and Translational Readiness
- Cell response prediction, toxicity screening & efficacy benchmarking
- Integrating discovery, delivery, and validation & Documentation and reproducibility
- Early regulatory thinking
- Hands-on:Build an end-to-end mini workflow: molecule/property prediction → delivery optimization → biological validation summary
- Tools: RDKit, Python, SHAP, Streamlit, GitHub, Power BI or simple dashboard tools
Important Dates
Registration Ends
04/25/2026
IST 7:00 PM
Workshop Dates
04/25/2026 – 04/27/2026
IST 8:00 PM
Workshop Outcomes
Participants will be able to:
- Understand how AI supports the full therapeutic pipeline from discovery to validation.
- Apply predictive models for screening and prioritizing candidates.
- Evaluate delivery strategies and formulation choices for target applications.
- Interpret validation data in a biologically meaningful and translational context.
- Propose integrated AI-assisted development workflows for real use-cases.
Fee Structure
Student Fee
₹2499 | $65
Ph.D. Scholar / Researcher Fee
₹3499 | $75
Academician / Faculty Fee
₹4499 | $90
Industry Professional Fee
₹5499 | $105
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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