Workshop Structure
Day 1: Protein Dysfunction, Structural Dynamics & Disease Mechanisms
- Protein folding, misfolding, aggregation, and intrinsically disordered proteins in neurodegenerative disease.
- Disease-associated proteins in Alzheimer’s disease, Parkinson’s disease, ALS, and related disorders.
- Sequence, domain, and structural analysis using experimental and predicted protein information.
- Identification of disordered and aggregation-prone regions.
- Disease-associated variant interpretation and mutation-impact analysis.
- AlphaFold-based structure interpretation, confidence assessment, and limitations for intrinsically disordered proteins.
- Concepts of protein stability, conformational dynamics, oligomerization, and molecular dynamics.
Tools: UniProt, RCSB PDB, AlphaFold DB, ClinVar, AlphaMissense, IUPred2A/AGGRESCAN, PyMOL/ChimeraX
Hands-on: Analyze a disease-associated protein for structural confidence, disorder and aggregation propensity, disease variants, and mutation-related effects.
Day 2: Candidate Biomarkers, Molecular Pathways & Network Biology
- Explore public neurobiology and neurodegenerative-disease transcriptomic datasets.
- Perform differential gene-expression analysis and identify disease-associated molecular signatures.
- Visualize disease patterns using PCA and UMAP.
- Perform functional enrichment and biological pathway analysis.
- Build protein–protein interaction networks and identify hub genes and molecular drivers.
- Prioritize candidate biomarkers using statistical and biological evidence.
- Apply machine-learning approaches such as Random Forest/XGBoost for biomarker ranking.
- Interpret important predictors using SHAP concepts.
- Overview of single-cell transcriptomics and its applications in neurological disease research.
Tools: GEO, GEO2R/DESeq2, Python/R, pandas, scikit-learn, UMAP, Enrichr, STRING, Cytoscape, XGBoost, SHAP
Hands-on: Identify differentially expressed genes, enriched pathways, network hubs, and ML-ranked candidate biomarkers from a neurobiology dataset.
Day 3: Therapeutic Target Prioritization & AI-Assisted Drug Discovery
- Translate protein, transcriptomic, pathway, and network evidence into therapeutic target candidates.
- Prioritize structurally suitable and biologically relevant drug targets.
- Retrieve and evaluate known compounds for drug repurposing and small-scale computational screening.
- Analyze drug–target interactions and molecular descriptors.
- Assess drug-likeness and pharmacokinetic properties.
- Perform ADMET prediction for candidate compounds.
- Conduct molecular docking using structurally characterized targets.
- Analyze binding interactions and docking poses.
- Integrate docking, ADMET, molecular descriptors, and biological evidence for candidate ranking.
- Apply AI-assisted approaches for therapeutic-candidate prioritization.
Tools: RCSB PDB, PubChem, ChEMBL/BindingDB, AutoDock Vina or CB-Dock2, PyMOL, SwissADME, Python
Hands-on: Prioritize a therapeutic target, screen selected compounds, evaluate docking and ADMET results, and rank candidate therapeutics using integrated computational evidence.
AI Integration in Healthcare Management
1 × INR ₹2,499.00 
AI as a Weapon in the Cyber World
1 × INR ₹2,499.00 
Introduction to Data Warehousing
1 × INR ₹199.00 
Organoids & 3D Culture Models for Drug Screening
1 × INR ₹2,499.00 
Introduction to AlphaFold
1 × INR ₹199.00 
Generative Adversarial Networks (GANs)
1 × INR ₹5,499.00 
Applied Nanobiotechnology for Sustainable Biochemicals and Biofuels
1 × INR ₹2,499.00 
Biological Sequence Analysis using R Programming
1 × INR ₹1,249.00 
Fundamentals of Computational Biology
1 × INR ₹199.00 
Diversity, Equity, and Inclusion (DEI)
1 × INR ₹2,499.00 
Speech Recognition and Processing Course
1 × INR ₹2,499.00 
Quantum Computing Basics
1 × INR ₹5,499.00 
Regression Analysis with Python
1 × INR ₹199.00 
