README.md

---
license: mit
metrics:
- mae
base_model:
- facebook/esm2_t33_650M_UR50D
pipeline_tag: tabular-regression
tags:
- PLM
- GBT
- ESM2
- Regression
---



## BindPred: Gradient Boosted Trees on ESM2 Embeddings

# Model Overview
The BindPred model is a Gradient Boosted Trees (GBT) regressor trained on ESM2 embeddings from Meta’s ESM2 protein language model. It is designed for binding affinity predictive tasks.
Pretrained Colab Notebook:https://colab.research.google.com/drive/1ndzICxVBUUBHffmi0KDtUXaKaMtqTz55

# Available Pretrianed Models:

ACE2_RBD_BindPred.json	 

Predicts binding affinity between ACE2 (human and animals) and RBD proteins.

ESM2_BindPred.json	

General-purpose GBT model trained on ESM2 embeddings.


# Model Details
•	Base Model: ESM2

•	Architecture: Gradient Boosted Trees (CatBoostRegressor)

•	Framework: CatBoost

•	Task: Regression

# How to Use

Download Model from Hugging Face

from huggingface_hub import hf_hub_download

# Download General model

model_path = hf_hub_download(repo_id="hbp5181/BindPred", filename="ESM2_BindPred.cbm")

Load Model in CatBoost

from catboost import CatBoostRegressor

model = CatBoostRegressor()

model.load_model(model_path, format="cbm")


# Training Details

•	Feature Extraction: ESM2 embeddings (33-layer transformer, 650M params)

•	Training Algorithm: CatBoost Gradient Boosting

•	Dataset: 

      ACE2 RBD: https://github.com/jbloomlab/SARSr-CoV_homolog_survey
      
      General: https://zenodo.org/records/14271435
      
•	Evaluation Metrics: RMSE, R^2

# Applications

•	Binding affinity predictions

# Limitations & Considerations

•	The model is trained on ESM2 embeddings and is limited by the quality of those embeddings.

•	Performance depends on the training dataset used.

•	Not a deep-learning model; instead, it leverages GBTs for fast, interpretable predictions.

# Citation

👤 Maintainer: hbp5181@psu.edu

📅 Last Updated: February 2025