About Workshop
This three-day workshop introduces lipid nanoparticles for mRNA and gene delivery, with emphasis on formulation design, machine-learning prediction, and multi-parameter optimisation. Participants will explore lipid properties, biological delivery barriers, formulation datasets, molecular descriptors, predictive modelling, SHAP-based interpretation, and candidate ranking. Guided exercises using LIPID MAPS, PubChem, RDKit, Python, scikit-learn, XGBoost, Optuna, Plotly, and Streamlit will lead to a preliminary LNP prediction dashboard.
Aim
To provide participants with practical knowledge of lipid nanoparticle formulation, AI-based delivery-performance prediction, and data-driven optimisation for mRNA and gene-delivery applications.
What Participants Will Learn
- 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.
Structure
📅 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:
LIPID MAPS, PubChem, formulation datasets, and data-curation templates📅 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:
Python, Google Colab, RDKit, Pandas, scikit-learn, XGBoost, and formulation datasets📅 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:
Python, XGBoost, Optuna, SHAP, Plotly, Streamlit, and candidate-ranking templatesImportant Dates
Registration Ends
4: 30 PM
Workshop Dates
2026-08-24
05:30 PM
05:30 PM
What You Will Gain
Outcomes
- 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.
Who Should Attend
- 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