AI-Powered Protein Engineering: From Molecular Discovery to Therapeutic Innovation

To provide practical, research-oriented training in AI-assisted protein engineering, combining structural bioinformatics, deep learning, generative AI, protein design, and computational validation for therapeutic and industrial protein development.

• Understand the principles of computational and AI-driven protein engineering.
• Analyse protein sequences, evolutionary conservation, and structure–function relationships.
• Apply AI-based approaches for protein structure prediction.
• Identify functional sites, binding pockets, and potential engineering regions.
• Understand protein language models and AI-based sequence representations.
• Explore generative AI for de novo protein and scaffold design.
• Perform protein mutation analysis and computational sequence optimisation.
• Explore AI-assisted antibody and therapeutic peptide engineering.
• Evaluate protein stability, solubility, specificity, and binding properties.
• Analyse protein–protein and protein–ligand interactions.
• Understand molecular docking and molecular-dynamics-based validation.
• Explore AI-driven enzyme engineering for biomedical and industrial applications.

• Graduate & Postgraduate Students – Biotechnology, Bioinformatics, Biochemistry, Molecular Biology, Chemistry, Pharmacy, Life Sciences, and related fields.
• PhD Scholars & Researchers – Protein engineering, structural biology, computational biology, drug discovery, antibody engineering, enzyme engineering, and therapeutic proteins.
• Academicians & Faculty – Professionals interested in AI-driven protein design, structural bioinformatics, and computational biotechnology.
• Biotechnology Professionals – Protein design, biologics, enzyme engineering, and biotechnology R&D.
• Pharmaceutical Professionals – Drug discovery, therapeutic proteins, antibody development, and biologics R&D.
• Bioinformatics & Computational Researchers – AI/ML, structural bioinformatics, molecular modelling, and computational protein design.
• R&D Professionals – Industrial enzyme development, protein optimisation, and next-generation biologics.

• Analyse protein sequences and evolutionary conservation.
• Predict and interpret protein structures using AI-based tools.
• Identify functional sites and potential engineering targets.
• Apply generative AI concepts to protein sequence design.
• Generate and evaluate protein variants.
• Explore computational antibody and peptide engineering.
• Assess protein stability, solubility, specificity, and binding properties.
• Analyse protein–protein and protein–ligand interactions.
• Understand docking and molecular-dynamics-based validation.
• Apply computational approaches to enzyme engineering.
• Evaluate therapeutic protein developability and immunogenicity considerations.
• Prioritise engineered protein candidates for experimental validation.
• Build an end-to-end computational protein-engineering workflow.