Update README.md

README.md

@@ -1,3 +1,36 @@
----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+task_categories:
+- feature-extraction
+language:
+- en
+tags:
+- chemistry
+- drug-discovery
+- cheminformatics
+- smiles
+- molecular-generation
+pretty_name: Curated Chemical Structures & Properties
+size_categories:
+- 1K<n<10K
+---
+### 🧪 Dataset Summary
+The **Curated Chemical Structures & Properties** dataset is a foundational resource for cheminformatics and AI-driven drug discovery. It provides highly standardized molecular representations (Canonical and Isometric SMILES, InChI, InChIKey) alongside computed physicochemical properties and ClassyFire taxonomic classifications.
+
+This dataset is ideal for training molecular generative models, predicting ADMET properties, or building structure-activity relationship (SAR) models.
+
+### 🚀 Key Features
+- **Standardized Representations**: Includes `canonical_smiles`, `isometric_smiles`, and `inchi_key` for robust molecular matching.
+- **Rich Physicochemical Features**: Pre-computed properties including Molecular Weight (`mwt`), LogP (`xlogp3`), Hydrogen Bond Donors (`hbd`), and Acceptors (`hba`).
+- **Deep Taxonomic Classification**: Features hierarchical ClassyFire annotations (Kingdom, Superclass, Class, Direct Parent) for chemical space analysis.
+
+### 💻 Quick Start & MCP Integration
+Integrate chemical structure search into your LLM workflows using our **Chemical Structure MCP Tool** on GitHub.
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("your-org/chemical-structures", split="train")
+print(dataset[0]["canonical_smiles"])
+# Output: 'CC1(C2=C(C=CC3=CC=CC=C32)C4=C1C(=NC=C4)C5=CC=CC=[C-]5)C'
+```