Feature Extraction
Transformers
Safetensors
seqscreen
proteins
molecules
bioinformatics
drug-discovery
custom_code
Instructions to use SaeedLab/BindScreen-Frozen-LIT_PCBA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SaeedLab/BindScreen-Frozen-LIT_PCBA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SaeedLab/BindScreen-Frozen-LIT_PCBA", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SaeedLab/BindScreen-Frozen-LIT_PCBA", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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- transformers
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---
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# BindScreen Frozen
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This model corresponds to the BindScreen frozen configuration, in which both encoders are frozen and only the projection layers are trained on filtered ChEMBL for
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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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# bindscreen
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bindscreen = AutoModel.from_pretrained(
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'SaeedLab/BindScreen-Frozen-
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trust_remote_code=True
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).eval()
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- transformers
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---
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# BindScreen Frozen LIT-PCBA
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This model corresponds to the BindScreen frozen configuration, in which both encoders are frozen and only the projection layers are trained on filtered ChEMBL for LIT-PCBA.
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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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# bindscreen
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bindscreen = AutoModel.from_pretrained(
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'SaeedLab/BindScreen-Frozen-LIT_PCBA',
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trust_remote_code=True
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).eval()
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