AI-Guided Lipid Nanoparticle Design for mRNA & Gene Delivery: Formulation Prediction and Optimization

• Understand the composition, properties, and biological functions of lipid nanoparticles.
• Explore lipid structures, formulation variables, and delivery-related data from public resources.
• Analyse molecular descriptors and formulation parameters using Python-based workflows.
• Apply machine-learning algorithms to predict delivery efficiency and formulation performance.
• Develop an interpretable LNP optimisation workflow and preliminary prediction dashboard.

📅 Day 1: Lipid Nanoparticle and mRNA Delivery Fundamentals

• Introduction to lipid nanoparticles for mRNA and gene delivery
• Key components of LNP formulations
• Ionisable lipids and endosomal escape
• Helper phospholipids and membrane stability
• Roles of cholesterol and PEG lipids
• Encapsulation efficiency, particle size, and surface properties
• Biological barriers affecting mRNA delivery
• Cellular uptake, intracellular trafficking, and cargo release
• Introduction to lipid and chemical databases
• Overview of LNP formulation datasets

🛠️ Hands-on:

• Explore lipid structures and properties using LIPID MAPS and PubChem
• Retrieve molecular information for selected LNP components
• Examine formulation variables such as lipid ratios, particle size, and encapsulation efficiency
• Prepare a structured lipid-formulation dataset for analysis

🧰 Tools Covered:

📅 Day 2: AI-Based Formulation Prediction

• Collection and organisation of lipid-formulation data
• Molecular representation and descriptor generation
• Data cleaning and missing-value treatment
• Encoding formulation and experimental variables
• Feature selection and dimensionality reduction concepts
• Delivery-efficiency and encapsulation prediction
• Regression and classification approaches for LNP modelling
• Comparison of machine-learning algorithms
• Model validation and performance metrics
• Interpretation of formulation–performance relationships

🛠️ Hands-on:

• Generate lipid molecular descriptors using RDKit
• Clean and preprocess formulation data using Pandas
• Train delivery-performance models using scikit-learn and XGBoost
• Compare model performance using suitable evaluation metrics
• Identify influential molecular and formulation features

🧰 Tools Covered:

📅 Day 3: LNP Optimisation, Interpretation and Candidate Selection

• Multi-parameter optimisation of LNP formulations
• Balancing delivery efficiency, encapsulation, stability, and toxicity
• Hyperparameter and formulation-space optimisation
• Prediction of delivery performance and potential toxicity
• SHAP-based model interpretation
• Identification of important lipid and formulation variables
• Candidate formulation scoring and ranking
• Data visualisation for formulation comparison
• Dashboard design for LNP prediction workflows
• Research and pharmaceutical-development applications

🛠️ Hands-on:

• Optimise model and formulation parameters using Optuna
• Interpret delivery predictions using SHAP
• Rank candidate LNP formulations using multiple performance criteria
• Create interactive formulation visualisations using Plotly
• Develop a basic LNP prediction dashboard prototype using Streamlit

🧰 Tools Covered:

• Explain the roles of ionisable lipids, helper lipids, cholesterol, and PEG lipids in LNP formulations.
• Prepare and preprocess lipid and formulation datasets for predictive analysis.
• Generate molecular descriptors and train comparative machine-learning models.
• Interpret formulation predictions using SHAP and optimise parameters using Optuna.
• Rank candidate LNP formulations and create a basic delivery-prediction dashboard.

• Graduate and postgraduate students in pharmacy, biotechnology, bioinformatics, chemistry, and life sciences
• PhD scholars and research fellows
• Academicians and faculty members
• Formulation scientists and pharmaceutical researchers
• Drug-delivery and nanomedicine researchers
• Biotechnology and pharmaceutical R&D professionals
• Researchers working in mRNA therapeutics, vaccines, gene therapy, or cancer immunotherapy