Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
The Course on Network Pharmacology Using Bioinformatics Tools is designed for students, researchers, and professionals in the biotech, pharma, and life sciences sectors. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
The Course on Network Pharmacology Using Bioinformatics Tools is designed for students, researchers, and professionals in the biotech, pharma, and life sciences sectors.
📋 Course Objectives
1. Put bioinformatics techniques to work on real datasets and case studies.
2. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in bioinformatics
• R&D engineers and working professionals applying bioinformatics in industry
• Academics and educators building research or teaching capacity in bioinformatics
🚀 Key Learning Outcomes
• A portfolio-grade bioinformatics deliverable you can defend and extend.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
From One Target to a Network
• The polypharmacology premise and where the single-target model breaks
• Traditional medicine and multi-component formulations as the common use case
• What a network result can support and the causal claims it cannot
Assembling Compound-Target Data
• SwissTargetPrediction, TCMSP and STITCH, and their differing evidence quality
• Predicted against experimentally validated interactions, kept clearly separate
• Oral bioavailability and drug-likeness filters and their arbitrary thresholds
Building the Disease Side
• GeneCards, OMIM, DisGeNET and the literature bias baked into all of them
• Intersecting compound targets with disease genes and the size effects that follow
• Why a large intersection is often a popularity artefact, not a finding
Construction and Analysis
• STRING for protein interactions and Cytoscape for construction and layout
• Degree, betweenness and the hub concept, plus the fragility of centrality ranking
• Module detection with MCODE and interpreting a cluster cautiously
Making the Result Mean Something
• Enrichment analysis with the correct background and multiple testing correction
• Molecular docking of key pairs as a follow-up, not as proof
• Experimental validation and the reason network pharmacology papers are often criticised
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | AutoDock |
| Covered Tool / Platform | PyRx |
| Covered Tool / Platform | ChemDraw |
| Covered Tool / Platform | Discovery Studio |
| Covered Tool / Platform | ADMET Predictor |
| Covered Tool / Platform | Python |
| Covered Tool / Platform | SPSS |
Frequently Asked Questions
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