Update README.md

README.md

@@ -7,4 +7,36 @@ tags:
 - biology
 - medical
 pretty_name: HuggingLigand Embedding Dataset
----
+---
+# HuggingLigand Dataset
+
+## Overview
+
+**HuggingLigand** is a deep learning pipeline developed to predict the binding affinity between proteins and ligands. This prediction task is essential in fields such as **drug discovery**, **biophysics**, and **computational biology**, where determining how strongly a small molecule ligand binds to a protein target is a key step in understanding molecular interactions and prioritizing drug candidates.
+
+The dataset provides precomputed embeddings for proteins and ligands, enabling efficient training and testing of machine learning models for binding affinity prediction.
+
+## Embedding Models Used
+
+- **ProtT5**: A transformer-based protein language model pretrained on millions of protein sequences.
+- **ChemBERTa**: A transformer-based molecular language model trained on SMILES representations of chemical compounds.
+
+Both models generate high-dimensional embeddings from raw sequence and molecular data. These embeddings are concatenated and used as input to a customizable regression model, which predicts continuous binding affinity values (e.g., **Kd**, **Ki**, or **IC50**).
+
+## Applications
+
+- Structure-free binding affinity prediction
+- Virtual screening and hit prioritization in drug discovery
+- Data-driven biophysical modeling
+- Benchmarking molecular embedding models
+
+## Dataset Structure
+
+The dataset contains:
+- Protein embeddings generated by **ProtT5**
+- Ligand embeddings generated by **ChemBERTa**
+- Binding affinity labels (e.g., Kd, Ki, IC50)
+
+## Citation
+
+If you use this dataset, please cite: