Machine Learning for Optimizing Lipid Nanoparticles (LNPs) in mRNA & Gene Delivery
Designing smarter LNPs with smarter algorithms.
About This Course
This workshop provides a comprehensive foundation in the rapidly evolving field of AI- and ML-driven optimization of Lipid Nanoparticles (LNPs) 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.
Workshop 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.
Workshop Structure
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 LNPs
- Factors affecting LNP performance:
- Size, charge, lipid ratios, encapsulation efficiency
- Understanding intracellular trafficking and endosomal escape
- Case examples: LNPs in mRNA vaccines & gene editing
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)
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
Who Should Enrol?
- 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.
Important Dates
Registration Ends
01/15/2026
IST 07:00 PM
Workshop Dates
01/15/2026 – 01/17/2026
IST 08:00 PM
Workshop Outcomes
By the end of the workshop, participants will be able to:
- Describe the structural and functional components of LNP formulations.
- Identify the factors that govern mRNA encapsulation, release, and intracellular trafficking.
- Explain the ML workflow for predicting LNP behavior.
- Interpret nano–bio interaction datasets relevant to mRNA delivery.
- Recognize the role of AI in improving next-generation gene delivery platforms.
Fee Structure
Student Fee
₹1799 | $70
Ph.D. Scholar / Researcher Fee
₹2799 | $80
Academician / Faculty Fee
₹3799 | $95
Industry Professional Fee
₹4799 | $110
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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