AI-Driven Phage Therapy: Designing Precision Antimicrobial Solutions Against Drug Resistance
From Phage Genomes to AI-Powered Host Prediction, Rational Cocktail Design & Antimicrobial Discovery
Skills you will gain:
About Program:
This 3-day hands-on workshop introduces participants to AI-assisted approaches for modern phage therapy research. It covers computational phage–host prediction, rational phage cocktail design, genomic safety screening, and discovery of phage-derived antimicrobial proteins.
Participants will work with genomic and protein sequence data using practical Python-based workflows that can be executed in Google Colab.
Aim: To provide researchers and professionals with practical skills to apply bioinformatics and machine learning in phage therapy, host prediction, cocktail optimisation, and antimicrobial candidate discovery.
Program Objectives:
- Understand the role of bacteriophages in combating antimicrobial resistance.
- Analyse phage genomic data for host prediction.
- Apply machine learning for phage–host classification.
- Design rational and resistance-aware phage cocktails.
- Screen phages for therapeutic suitability and genomic safety.
- Identify and prioritise phage-derived antimicrobial proteins.
- Develop reproducible computational workflows using Google Colab.
What you will learn?
📅 Day 1: AI-Based Phage–Host Prediction
- Introduction to bacteriophage therapy and antimicrobial resistance
- Lytic vs temperate phages and therapeutic suitability
- Understanding phage–host specificity and host range
- Genomic signals for host prediction:
- k-mer patterns
- GC content
- CRISPR spacer matches
- sequence similarity
- codon usage
- Introduction to AI/ML-based phage–host prediction
- Overview of tools: iPHoP, CHERRY, PhaBOX and RaFAH
- Prediction confidence and interpretation
🛠️ Hands-On: Phage–Host Prediction
- Extract genomic features from phage FASTA sequences
- Train an ML model to predict bacterial hosts
- Generate host probability and confidence scores
🧰 Tools: Python, Biopython, pandas, scikit-learn
🗓️ Day 2: Rational Phage Cocktail Design
- Why single-phage therapy may fail
- Host-range and bacterial susceptibility matrices
- Phage resistance and receptor diversity
- Genomic safety screening of candidate phages
- Removing temperate, virulence and AMR-associated candidates
- Host coverage and redundancy analysis
- Resistance-aware phage selection
- Set-cover and optimisation approaches for cocktail design
- Phage–antibiotic synergy and sequential therapy concepts
🛠️ Hands-On: Optimise a Phage Cocktail
- Analyse a phage–bacteria host-range matrix
- Apply safety and coverage filters
- Select an optimised multi-phage combination
🧰 Tools: Python, pandas, NumPy, SciPy / NetworkX
🗓️ Day 3: AI-Guided Phage-Derived Antimicrobial Discovery
- Phages as sources of antimicrobial molecules
- Endolysins and phage-derived lysins
- Depolymerases and biofilm-targeting enzymes
- Protein sequence-based antimicrobial discovery
- Amino-acid and physicochemical feature extraction
- Introduction to protein embeddings and protein language models
- AI-based classification of phage proteins
- Candidate scoring and prioritisation
- Translational applications in AMR and precision antimicrobial therapy
🛠️ Hands-On: Antimicrobial Protein Prioritisation
- Analyse phage protein sequences
- Apply ML-based protein classification
- Rank endolysin/depolymerase candidates
🧰 Tools: Python, Biopython, pandas, scikit-learn, ESM/Hugging Face
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
This workshop is suitable for:
- Researchers and Research Scholars
- PhD and Postgraduate Students
- Academicians and Faculty Members
- Microbiologists and Molecular Biologists
- Bioinformatics and Computational Biology Researchers
- Antimicrobial Resistance Researchers
- Infectious Disease Researchers
- Biotechnology and Pharmaceutical Professionals
- Researchers working in Phage Biology, Microbial Genomics, Viromics and Alternative Antimicrobial Strategies
Career Supporting Skills
Program Outcomes
By the end of the workshop, participants will be able to:
- Predict probable bacterial hosts from phage genomic features.
- Interpret host-range and prediction confidence data.
- Construct phage–bacteria interaction matrices.
- Optimise multi-phage cocktails based on host coverage and resistance considerations.
- Apply genomic safety filters to candidate phages.
- Analyse phage protein sequences for antimicrobial potential.
- Prioritise endolysin and depolymerase candidates using AI/ML approaches.
- Apply computational phage therapy workflows in research projects.