# 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 inp…
# 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 inp…
