Skills you will gain:
About Program:
This workshop provides a comprehensive foundation in the rapidly evolving field of AI- and ML-driven optimization of Lipid Nanoparticles used in mRNA and gene delivery systems. Participants will explore how machine learning accelerates the design, formulation, and performance prediction of LNPs by analyzing physicochemical descriptors, biodistribution data, lipid composition ratios, and delivery efficiency metrics. Through interactive lectures and conceptual demonstrations, attendees will understand how computational models guide next-generation gene therapies, vaccines, and nucleic acid delivery technologies.
Aim:
To introduce learners to machine learning approaches used to design, optimize, and predict the performance of LNPs for mRNA and gene delivery applications.
Program Objectives:
Participants will:
- Understand the fundamentals of LNP structure, components, and formulation principles.
- Learn how machine learning models are applied to predict LNP delivery efficiency and stability.
- Explore key physicochemical descriptors influencing mRNA encapsulation and cellular uptake.
- Analyze datasets related to LNP composition, transfection efficiency, and toxicity.
- Understand how AI accelerates preclinical mRNA/gene therapy development.
What you will learn?
Day 1 – Fundamentals of LNPs for mRNA & Gene Delivery
- LNPs: Structure and key components
- Ionizable lipids, helper lipids, cholesterol, PEG-lipids
- Mechanism of mRNA and gene delivery via LNP
- Factors affecting LNP performance:
- Size, charge, lipid ratios, encapsulation efficiency
- Understanding intracellular trafficking and endosomal escape
- Case examples: LNPs in mRNA vaccines & gene editing
Focus:
Understanding the biochemical and functional foundations required before applying ML.
Day 2 – Machine Learning Approaches for LNP Optimization
- Why machine learning for LNP design?
- Dataset types: structural, compositional, biological response
- Physicochemical descriptors for LNPs
- Basic ML Models:
- Regression (predicting encapsulation efficiency, particle size)
- Classification (toxicity, uptake efficiency)
- Neural networks (performance prediction)
- Feature selection & descriptor engineering
- Example ML workflow (conceptual Python demonstration)
Focus:
Building intuition for dataset processing and model prediction in LNP design.
Day 3 – AI-Enhanced Decision Making for Next-Gen Gene Delivery Systems
- Predictive modeling for delivery efficiency & stability
- Optimization of lipid ratios using ML-derived insights
- Interpreting model outputs for actionable formulation decisions
- ML-assisted screening of ionizable lipids
- Challenges & future outlook:
- Data limitations
- Transfer learning
- Personalized LNP formulations
- Emerging directions: AI-assisted CRISPR delivery, self-amplifying mRNA systems
Focus:
Translating ML results into practical formulation strategies for real-world mRNA/gene delivery applications.
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- UG & PG students in Biotechnology, Nanotechnology, Biomedical Sciences, Pharmacy, Chemical Engineering, Life Sciences.
- PhD scholars working in nanomedicine, drug delivery, AI/ML, or gene therapy.
- Academicians interested in emerging nanotechnology-AI integrations.
- Industry professionals from pharma, biotech, nucleic acid therapeutics, and R&D units.
Career Supporting Skills
Program Outcomes
- Understand the structure, components, and mechanisms of LNPs for mRNA and gene delivery.
- Explain the key physicochemical parameters (size, charge, composition) that govern LNP performance.
- Relate LNP formulation variables to encapsulation efficiency, stability, and transfection efficiency.
- Describe how machine learning workflows are applied to LNP optimization.
- Identify and interpret physicochemical descriptors and biological readouts used in ML models for LNPs.
- Analyze simple LNP-related datasets to derive trends in delivery efficiency and toxicity.
- Evaluate the advantages and limitations of AI/ML approaches in nucleic acid delivery research.
- Gain awareness of current research directions, applications, and industry relevance of LNP-based mRNA/gene therapies.