Platforms for Screening and Building Ligand Libraries in Drug Discovery & Bioinformatics

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During my computational drug discovery projects, I came across several valuable databases and platforms that help researchers identify, collect, and screen ligand libraries for molecular docking, virtual screening, and pharmacological studies.

📌 1. IMPPAT Database

* Indian Medicinal Plants, Phytochemistry And Therapeutics database
* Excellent source of phytochemicals from Indian medicinal plants
* Provides structures, physicochemical properties, and therapeutic information

📌 2. PubChem

* One of the world’s largest chemical databases
* Download compounds in SDF, SMILES, and 3D formats
* Useful for structure retrieval and compound exploration

📌 3. ZINC20 Database

* Ready-to-dock purchasable compounds
* Supports virtual screening campaigns
* Millions of commercially available molecules

📌 4. ChEMBL

* Bioactive molecules with experimentally validated activity data
* Ideal for lead identification and drug repurposing studies

📌 5. NPASS Database

* Natural Product Activity and Species Source Database
* Links natural compounds with biological activities and source organisms

📌 6. COCONUT Database

* Collection of Open Natural Products
* Large repository of natural compounds suitable for virtual screening

📌 7. FooDB

* Comprehensive database of food-derived compounds
* Useful for nutraceutical and functional food research

📌 8. DrugBank

* FDA-approved and investigational drugs
* Valuable for drug repurposing and reference ligand studies

📌 9. SuperNatural 3.0

* Large collection of natural compounds
* Suitable for natural-product-based virtual screening

📌 10. BindingDB

* Experimental protein–ligand binding affinity data
* Useful for validating docking and screening results

💡 Typical Workflow
Plant Selection → Ligand Collection (IMPPAT/NPASS/PubChem) → Ligand Preparation → Molecular Docking → ADMET Analysis → Molecular Dynamics Simulation → MM/PBSA Binding Energy Analysis

These resources have become indispensable in modern in-silico drug discovery, enabling researchers to rapidly identify and prioritize promising lead molecules before experimental validation.

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