AI-Guided CRISPR/Cas9 Delivery: Nanoparticles for Non-Viral Gene Editing
From Guide RNA Design to Smart Nanoparticle Delivery — Use AI to Optimize Non-Viral CRISPR/Cas9 Gene-Editing Systems.
About This Course
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.
Aim
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.
Workshop Objectives
- 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.
Workshop Structure
📅 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
Who Should Enrol?
- 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
Important Dates
Registration Ends
October 15, 2026
IST 4: 30 PM
Workshop Dates
October 15, 2026 – October 17, 2026
IST 5:30 PM
Workshop Outcomes
- 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.
Meet Your Mentor(s)
Fee Structure
Student Fee
₹2199 | $59
Ph.D. Scholar / Researcher Fee
₹3199 | $74
Academician / Faculty Fee
₹4499 | $89
Industry Professional Fee
₹5999 | $109
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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