Feature Extraction
PEFT
Safetensors
Transformers
proteins
molecules
bioinformatics
drug-discovery
lora
Instructions to use SaeedLab/BindScreen-lora-LIT_PCBA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SaeedLab/BindScreen-lora-LIT_PCBA with PEFT:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("facebook/esm2_t36_3B_UR50D") model = PeftModel.from_pretrained(base_model, "SaeedLab/BindScreen-lora-LIT_PCBA") - Transformers
How to use SaeedLab/BindScreen-lora-LIT_PCBA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SaeedLab/BindScreen-lora-LIT_PCBA")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SaeedLab/BindScreen-lora-LIT_PCBA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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library_name: peft
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# BindScreen - ESM2 LoRA Adapter
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This repository contains the LoRA adapter weights for the protein encoder used in BindScreen, trained on filtered ChEMBL. BindScreen is a sequence-based virtual screening method built on a dual-encoder contrastive architecture. The adapter fine-tunes [ESM2 T36](https://huggingface.co/facebook/esm2_t36_3B_UR50D) on protein-molecule interaction task.
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The projection layers are available separately at [SaeedLab/BindScreen-Finetuning](https://huggingface.co/SaeedLab/BindScreen-Finetuning), which also contains the full model description, architecture diagram, and usage examples.
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\[[Github Repo](https://github.com/pcdslab/BindScreen)\] | \[[Dataset on HuggingFace](https://huggingface.co/datasets/SaeedLab/BindScreen)\] | \[[Model Collection](https://huggingface.co/collections/SaeedLab/bindscreen)\] | \[[Cite](#citation)\]
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## Model Details
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This adapter corresponds to the **BindScreen-Finetuning** configuration, in which ESM2 T36 is fine-tuned via LoRA alongside the projection layers.
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- [BindScreen-Frozen](https://huggingface.co/SaeedLab/BindScreen-Frozen): only the projection layers are trained, both encoders are frozen.
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- [BindScreen-Finetuning](https://huggingface.co/SaeedLab/BindScreen-Finetuning): the projection layers and ESM2 T36 are trained via LoRA, MolDeBERTa MLC is frozen.
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## Usage
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This adapter must be used together with [SaeedLab/BindScreen-Finetuning](https://huggingface.co/SaeedLab/BindScreen-Finetuning), which provides the projection layers. The full usage example, including molecule encoding and similarity computation, is available in that repository.
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### Dependencies
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library_name: peft
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# BindScreen - ESM2 LoRA Adapter - LIT-PCBA
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This repository contains the LoRA adapter weights for the protein encoder used in BindScreen, trained on filtered ChEMBL for LIT-PCBA. BindScreen is a sequence-based virtual screening method built on a dual-encoder contrastive architecture. The adapter fine-tunes [ESM2 T36](https://huggingface.co/facebook/esm2_t36_3B_UR50D) on protein-molecule interaction task.
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The projection layers are available separately at [SaeedLab/BindScreen-Finetuning-LIT_PCBA](https://huggingface.co/SaeedLab/BindScreen-Finetuning-LIT_PCBA), which also contains the full model description, architecture diagram, and usage examples.
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\[[Github Repo](https://github.com/pcdslab/BindScreen)\] | \[[Dataset on HuggingFace](https://huggingface.co/datasets/SaeedLab/BindScreen)\] | \[[Model Collection](https://huggingface.co/collections/SaeedLab/bindscreen)\] | \[[Cite](#citation)\]
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## Model Details
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This adapter corresponds to the **BindScreen-Finetuning-LIT_PCBA** configuration, in which ESM2 T36 is fine-tuned via LoRA alongside the projection layers.
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## Usage
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This adapter must be used together with [SaeedLab/BindScreen-Finetuning-LIT_PCBA](https://huggingface.co/SaeedLab/BindScreen-Finetuning-LIT_PCBA), which provides the projection layers. The full usage example, including molecule encoding and similarity computation, is available in that repository.
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### Dependencies
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