AI-Powered CRISPR/Cas9 Nanoparticle Delivery: Non-Viral Gene Editing

This 3-day virtual hands-on workshop introduces participants to the integration of CRISPR/Cas9 gene editing, non-viral nanoparticle delivery, and AI-based formulation optimization. Participants will learn how to design and evaluate guide RNAs, explore nanoparticle and molecular properties, build AI-ready delivery datasets, and use machine learning to predict gene-delivery performance and interpret key formulation parameters.

The aim of this workshop is to equip participants with practical knowledge of CRISPR/Cas9 target design, nanoparticle-based non-viral delivery, and AI-assisted delivery optimization for gene-editing applications.

• Understand CRISPR/Cas9 editing principles and major non-viral delivery strategies.
• Design and evaluate guide RNAs using computational CRISPR tools.
• Explore nanoparticle composition, physicochemical properties, and cargo-delivery mechanisms.
• Build machine-learning models for predicting CRISPR delivery performance.
• Apply explainable AI to identify formulation parameters influencing efficiency and safety.

📅 Day 1: CRISPR/Cas9 Target Design, Guide RNA Selection & Off-Target Analysis

• CRISPR/Cas9 mechanism, sgRNA design, PAM recognition, and gene-editing applications Principles of guide-RNA selection and on-target efficiency GC content, specificity, and guide-quality metrics Off-target prediction and genome-editing safety Comparative use of CRISPOR and CHOPCHOP Selection of high-quality guides for therapeutic delivery
• 🛠️ Hands-on:
• Select a target gene Design guide RNAs using CRISPOR Compare guide candidates using CHOPCHOP Evaluate on-target and off-target scores Rank guide RNAs for nanoparticle delivery
• 🧰 Tools Covered:
• CRISPOR, CHOPCHOP, genomic sequence resources, and guide-ranking templates

📅 Day 2: Nanoparticle Design for Non-Viral CRISPR Delivery

• Viral versus non-viral CRISPR delivery Delivery of DNA, mRNA, sgRNA, and Cas9 RNPs Lipid, polymeric, hybrid, and inorganic nanoparticle systems Particle size, surface charge, composition, stability, and cargo loading Cellular uptake, endosomal escape, and targeted delivery Cytotoxicity, immune response, and biocompatibility Structure–property relationships for CRISPR nanocarriers Preparation of formulation data for AI modelling
• 🛠️ Hands-on:
• Retrieve nanoparticle-related molecular data from PubChem Generate descriptors and fingerprints using RDKit Combine molecular and formulation properties Visualise structure–property relationships Prepare an AI-ready nanoparticle formulation dataset
• 🧰 Tools Covered:
• PubChem, RDKit, Google Colab, Python, pandas, NumPy, Matplotlib, and formulation datasets

📅 Day 3: AI Prediction, Explainable Optimization & CRISPR Delivery Prioritization

• Machine learning for nanoparticle formulation screening Delivery-efficiency and safety prediction Feature engineering for molecular and formulation parameters Random Forest and XGBoost modelling Model validation and performance evaluation Explainable AI using SHAP Identification of key drivers of delivery efficiency and toxicity Multi-parameter formulation prioritization Translational considerations in non-viral CRISPR delivery
• 🛠️ Hands-on:
• Train an XGBoost model for delivery-performance prediction Evaluate predictive accuracy Interpret important features using SHAP Compare nanoparticle candidates by predicted efficiency and safety Integrate guide-RNA quality with formulation performance Rank CRISPR delivery candidates for further validation
• 🧰 Tools Covered:
• Google Colab, Python, RDKit, XGBoost, SHAP, pandas, scikit-learn, Matplotlib, and formulation-ranking templates

• Explain the CRISPR/Cas9 editing mechanism and major delivery challenges.
• Design and compare guide RNAs using CRISPOR and CHOPCHOP.
• Assess on-target efficiency and predicted off-target effects.
• Understand major nanoparticle systems used for non-viral CRISPR delivery.
• Evaluate formulation properties influencing cellular uptake and cargo delivery.
• Retrieve molecular information from PubChem.
• Generate molecular descriptors using RDKit.
• Prepare AI-ready nanoparticle formulation datasets.
• Train XGBoost models for delivery-performance prediction.
• Evaluate predictive models using appropriate performance metrics.

Graduate and Postgraduate Students in Biotechnology, Nanotechnology, Genetics, Molecular Biology, Bioinformatics, Biomedical Sciences, Pharmaceutical Sciences, and related disciplines PhD Scholars and Researchers working in CRISPR/Cas systems, gene therapy, nanomedicine, drug/gene delivery, biomaterials, or computational biology Academicians and Faculty Members interested in genome engineering, nanobiotechnology, translational research, and AI-assisted therapeutic development Industry Professionals from biotechnology, pharmaceutical, gene-therapy, nanomedicine, formulation, preclinical R&D, and AI/ML drug-development teams Basic knowledge of molecular biology or biotechnology is recommended; prior experience with CRISPR, Python, or machine learning is helpful but not mandatory