Feature Extraction
PEFT
Safetensors
Transformers
proteins
molecules
bioinformatics
drug-discovery
lora
Instructions to use SaeedLab/SeqScreen-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SaeedLab/SeqScreen-lora with PEFT:
Task type is invalid.
- Transformers
How to use SaeedLab/SeqScreen-lora with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SaeedLab/SeqScreen-lora")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SaeedLab/SeqScreen-lora", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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# SeqScreen - ESM2 LoRA Adapter
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This repository contains the LoRA adapter weights for the protein encoder used in SeqScreen, trained on filtered ChEMBL
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The projection layers are available separately at [SaeedLab/SeqScreen-Finetuning](https://huggingface.co/SaeedLab/SeqScreen-Finetuning), which also contains the full model description, architecture diagram, and usage examples.
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# SeqScreen - ESM2 LoRA Adapter
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This repository contains the LoRA adapter weights for the protein encoder used in SeqScreen, trained on filtered ChEMBL. SeqScreen 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/SeqScreen-Finetuning](https://huggingface.co/SaeedLab/SeqScreen-Finetuning), which also contains the full model description, architecture diagram, and usage examples.
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