AI-Powered Lipid Nanoparticle Design for RNA Therapeutics & Gene Delivery

This 3-day mentor-led workshop introduces computational and AI-assisted approaches for designing lipid nanoparticles (LNPs) for RNA therapeutics and gene delivery. Participants will explore LNP composition, physicochemical properties, RNA encapsulation, delivery mechanisms, formulation optimisation, and predictive modelling. Through guided computational exercises, they will learn how molecular and formulation features can be used to predict delivery performance and prioritise promising LNP candidates for further experimental validation.

To provide practical, research-oriented training in AI-assisted LNP design , combining lipid chemistry, formulation science, computational modelling, machine learning, and multi-parameter optimisation for RNA and gene delivery applications.

• Understand LNP architecture, lipid components, and RNA-delivery mechanisms.
• Explore ionizable lipids, helper phospholipids, cholesterol, and PEG-lipids.
• Understand how lipid composition and physicochemical properties influence delivery.
• Analyse LNP formulation and RNA-encapsulation parameters.
• Develop computational descriptors for lipid and formulation properties.
• Build datasets for LNP performance prediction.
• Apply machine-learning approaches to predict delivery-related outcomes.
• Identify important formulation features using explainable AI.
• Optimise LNP compositions using data-driven approaches.
• Prioritise candidate formulations for further experimental validation.

📅 Day 1: LNP Design, Lipid Chemistry & RNA Delivery

• Focus: Understand LNP architecture, lipid composition, formulation properties, and mechanisms of RNA delivery.
• LNP architecture and roles of ionizable lipids, phospholipids, cholesterol, and PEG-lipids Lipid physicochemical properties and structure–property–performance relationships RNA encapsulation, cellular uptake, endosomal escape, and intracellular release LNP applications in mRNA, siRNA, miRNA, and gene delivery Key formulation parameters: particle size, PDI, encapsulation efficiency, and delivery performance Introduction to computational and AI-guided LNP design
• 🛠️ Hands-on:
• Analyse curated LNP datasets, generate molecular and formulation descriptors, compare LNP compositions, and prepare an ML-ready dataset.
• Workflow: Lipid Chemistry → LNP Formulation → RNA Encapsulation → Delivery Performance
• 🧰 Tools Covered:
• PubChem, RDKit, Python, pandas, NumPy, Google Colab

📅 Day 2: Machine Learning for LNP Performance Prediction

• Focus: Develop predictive models linking lipid composition and formulation features with LNP performance.
• LNP dataset curation, preprocessing, and feature engineering Molecular and formulation descriptors for predictive modelling Prediction of particle size, PDI, encapsulation efficiency, and delivery-related outcomes Random Forest and XGBoost-based predictive modelling Model training, validation, and cross-validation Performance evaluation using R², MAE, RMSE, and classification metrics Feature importance and identification of key formulation determinants
• 🛠️ Hands-on:
• Build and validate an ML model for LNP performance prediction and identify formulation features influencing delivery.
• Workflow: LNP Dataset → Feature Engineering → ML Modelling → Validation → Feature Interpretation → Prediction
• 🧰 Tools Covered:
• Python, RDKit, pandas, scikit-learn, Random Forest, XGBoost, Google Colab

📅 Day 3: AI-Guided LNP Optimisation & Candidate Prioritisation

• Focus: Apply AI, explainable modelling, and multi-objective optimisation to prioritise promising LNP formulations.
• AI-guided optimisation of LNP composition and formulation parameters Prediction of performance for new and unseen formulations Explainable AI and SHAP-based interpretation of model predictions Virtual screening of LNP formulation libraries Multi-objective optimisation and candidate ranking Balancing formulation stability, encapsulation, and delivery performance RNA-specific delivery considerations and translational challenges Computational prediction, experimental validation, and model limitations
• 🛠️ Hands-on:
• Screen virtual LNP formulations, interpret model predictions, optimise formulation parameters, and generate a prioritised candidate shortlist for experimental validation.
• Workflow: LNP Library → AI Prediction → Explainable AI → Optimisation → Candidate Ranking → Experimental Validation
• 🧰 Tools Covered:
• Python, RDKit, XGBoost, SHAP, scikit-learn, pandas, Plotly, Google Colab

• Explain LNP architecture and RNA-delivery mechanisms.
• Distinguish the roles of major LNP lipid components.
• Relate lipid chemistry and formulation parameters to delivery performance.
• Develop and curate LNP formulation datasets.
• Generate molecular and formulation descriptors.
• Apply machine learning to LNP performance prediction.
• Evaluate and validate predictive models.
• Interpret formulation–performance relationships using explainable AI.
• Perform virtual screening of candidate LNP formulations.
• Apply multi-parameter optimisation to LNP design.
• Prioritise promising formulations for experimental validation.
• Understand computational limitations and translational considerations in LNP development.

Graduate and postgraduate students in Biotechnology, Biochemistry, Pharmacy, Chemistry, Nanotechnology, Bioinformatics, Life Sciences, and related disciplines. PhD scholars and researchers working in RNA therapeutics, drug delivery, nanomedicine, lipid chemistry, pharmaceutical sciences, biotechnology, and computational research. Academicians and faculty members interested in AI-assisted drug delivery, nanomedicine, RNA-based therapeutics, and computational formulation design. Industry professionals from pharmaceutical, biotechnology, RNA therapeutics, vaccine development, nanomedicine, formulation R&D, and drug-delivery sectors. Computational researchers and data scientists interested in applying machine learning to lipid nanoparticle design and therapeutic delivery.