| --- |
| license: mit |
| metrics: |
| - mae |
| base_model: |
| - facebook/esm2_t33_650M_UR50D |
| pipeline_tag: tabular-regression |
| tags: |
| - PLM |
| - GBT |
| - ESM2 |
| - Regression |
| --- |
| |
|
|
| ## BindPred: A Framework for Predicting Protein-Protein Binding Affinity from Language Model Embeddings |
|
|
| https://doi.org/10.1093/bioinformatics/btag309 |
|
|
| # 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 |
| |
| # Citation |
| Maintainer: hbp5181@psu.edu |
| |
| Last Updated: 3/13/2026 |
| |
| |
| |
