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--- |
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license: mit |
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metrics: |
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- mae |
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base_model: |
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- facebook/esm2_t33_650M_UR50D |
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pipeline_tag: tabular-regression |
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tags: |
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- PLM |
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- GBT |
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- ESM2 |
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- Regression |
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--- |
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## BindPred: Gradient Boosted Trees on ESM2 Embeddings |
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# Model Overview |
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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. |
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Pretrained Colab Notebook:https://colab.research.google.com/drive/1ndzICxVBUUBHffmi0KDtUXaKaMtqTz55 |
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# Available Pretrianed Models: |
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ACE2_RBD_BindPred.json |
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Predicts binding affinity between ACE2 (human and animals) and RBD proteins. |
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ESM2_BindPred.json |
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General-purpose GBT model trained on ESM2 embeddings. |
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# Model Details |
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• Base Model: ESM2 |
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• Architecture: Gradient Boosted Trees (CatBoostRegressor) |
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• Framework: CatBoost |
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• Task: Regression |
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# How to Use |
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Download Model from Hugging Face |
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from huggingface_hub import hf_hub_download |
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# Download General model |
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model_path = hf_hub_download(repo_id="hbp5181/BindPred", filename="ESM2_BindPred.cbm") |
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Load Model in CatBoost |
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from catboost import CatBoostRegressor |
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model = CatBoostRegressor() |
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model.load_model(model_path, format="cbm") |
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# Training Details |
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• Feature Extraction: ESM2 embeddings (33-layer transformer, 650M params) |
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• Training Algorithm: CatBoost Gradient Boosting |
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• Dataset: |
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ACE2 RBD: https://github.com/jbloomlab/SARSr-CoV_homolog_survey |
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General: https://zenodo.org/records/14271435 |
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• Evaluation Metrics: RMSE, R^2 |
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# Applications |
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• Binding affinity predictions |
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# Limitations & Considerations |
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• The model is trained on ESM2 embeddings and is limited by the quality of those embeddings. |
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• Performance depends on the training dataset used. |
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• Not a deep-learning model; instead, it leverages GBTs for fast, interpretable predictions. |
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# Citation |
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👤 Maintainer: [email protected] |
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📅 Last Updated: February 2025 |
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