Project-Glucocard

🧬 Project GlucoCard

Computational Drug Screening & Analysis Pipeline

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📍 Overview

GlucoCard is a Python-based computational pipeline designed to explore drug screening and analysis for potential Type-2 Diabetes inhibitors.

The project integrates rule-based evaluation with real molecular data to understand how computational approaches can assist in early-stage drug discovery.

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🫧 Pipeline Workflow

🔹 Data Acquisition

• Automated retrieval of molecular data using PubChemPy
• Extraction of molecular weight, H-bond donors, and acceptors

🔹 Drug-Likeness Screening

• Applied Lipinski-inspired rules
• Evaluated physicochemical suitability of compounds

🔹 Comparative Analysis

• Compared multiple drug candidates
• Generated ranking and recommendations

🔹 Bioavailability Concept (Exploratory)

• Studied BOILED-Egg model using SwissADME
• Understood GI absorption and drug-likeness concepts

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🌐 Case Study Molecules

• Berberine
• Acarbose

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📝 Key Observations:

• Berberine showed favorable drug-likeness properties
• Acarbose exceeded some Lipinski thresholds

• Computational screening helps in early filtering of drug candidates

• Click here to view the Screening Output Log (output_log.txt)

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##● Note: This project is a rule-based and exploratory computational model.
Docking and binding affinity analysis were studied conceptually and not fully implemented.

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🧪 Technologies Used

• Python
• PubChemPy
• SwissADME (conceptual use)

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🔮 Future Scope

• Molecular docking integration (AutoDock Vina)
• Machine learning-based prediction models
• ADMET property analysis
• Expansion to larger compound datasets

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Author

Thrishitha Reddy