Computational Neurobiology with AI: Protein Dynamics, Biomarkers, Molecular Pathways & Drug Discovery
From Protein Dysfunction to AI-Driven Biomarker Discovery and Therapeutic Design
Skills you will gain:
About Program:
This three-day workshop introduces participants to computational and AI-driven approaches used to study neurological and neurodegenerative diseases. The program begins with protein structure, mutation effects, and disease-associated molecular changes, then progresses to transcriptomics, biomarker discovery, pathway enrichment, and network biology. Participants will finally explore therapeutic target identification, drug repurposing, molecular docking, ADMET assessment, and AI-assisted candidate prioritization through guided hands-on workflows.
Aim: To provide participants with an integrated understanding of computational neurobiology by connecting protein-level disease mechanisms, biomarker discovery, pathway and network analysis, and AI-assisted therapeutic discovery for neurological and neurodegenerative disorders.
Program Objectives:
- Understand molecular and protein-level mechanisms associated with neurological disorders.
- Analyze disease-associated proteins, mutations, structural changes, and stability.
- Explore neurobiological datasets for biomarker discovery.
- Perform differential expression and disease-signature analysis.
- Apply pathway enrichment and network biology approaches.
- Identify hub genes, molecular drivers, and therapeutic targets.
- Use AI/ML approaches for biomarker and disease-pattern analysis.
- Explore drug repurposing and computational compound screening.
- Evaluate drug candidates using docking, drug-likeness, and ADMET parameters.
- Integrate structural, omics, network, and drug-discovery evidence into a unified workflow.
What you will learn?
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.
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- Biotechnology and Life Sciences students
- Neuroscience and Neurobiology researchers
- Bioinformatics and Computational Biology learners
- Molecular Biology and Biochemistry researchers
- Pharmacology and Drug Discovery researchers
- Biomedical Science students and professionals
- Genetics and Genomics researchers
- Pharmaceutical Sciences students
- PhD scholars and postdoctoral researchers
- Faculty members and research professionals
Career Supporting Skills
Program Outcomes
- Analyze disease-associated proteins and structural alterations.
- Interpret mutation and protein-stability predictions.
- Identify candidate biomarkers from neurobiological datasets.
- Perform pathway enrichment and protein–protein interaction analysis.
- Recognize key disease-associated genes, proteins, and molecular pathways.
- Apply basic machine-learning approaches for biomarker prioritization.
- Identify and prioritize potential therapeutic targets.
- Retrieve and evaluate candidate compounds for neurological disorders.
- Perform basic molecular docking and interaction analysis.
- Assess drug-likeness and ADMET properties.
- Build an integrated workflow from disease mechanism to biomarker, therapeutic target, and drug candidate prioritization.