Feature Extraction
Transformers
Safetensors
seqscreen
proteins
molecules
bioinformatics
drug-discovery
custom_code
Instructions to use SaeedLab/BindScreen-Finetuning-LIT_PCBA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SaeedLab/BindScreen-Finetuning-LIT_PCBA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SaeedLab/BindScreen-Finetuning-LIT_PCBA", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SaeedLab/BindScreen-Finetuning-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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# BindScreen Finetuning
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This model corresponds to the BinScreen finetuning configuration, in which ESM2 T36 and the projection layers are trained on filtered ChEMBL.
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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-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, MolDeBERTa MLC is frozen.
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## Usage
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backbone = PeftModel.from_pretrained(
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backbone,
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'SaeedLab/BindScreen-lora'
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).eval()
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- transformers
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# BindScreen Finetuning LIT-PCBA
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This model corresponds to the BinScreen finetuning configuration, in which ESM2 T36 and 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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Four configurations are available in this collection:
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- [BindScreen-Frozen-LIT_PCBA](https://huggingface.co/SaeedLab/BindScreen-Frozen-LIT_PCBA): only the projection layers are trained, both encoders are frozen. LIT-PCBA.
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- [BindScreen-Finetuning-LIT_PCBA](https://huggingface.co/SaeedLab/BindScreen-Finetuning-LIT_PCBA): the projection layers and ESM2 T36 are trained, MolDeBERTa MLC is frozen. LIT-PCBA.
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- [BindScreen-Frozen-DUDE](https://huggingface.co/SaeedLab/BindScreen-Frozen-LIT_PCBA): only the projection layers are trained, both encoders are frozen. DUDE.
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- [BindScreen-Finetuning-DUDE](https://huggingface.co/SaeedLab/BindScreen-Finetuning-LIT_PCBA): the projection layers and ESM2 T36 are trained, MolDeBERTa MLC is frozen. DUDE.
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## Usage
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backbone = PeftModel.from_pretrained(
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backbone,
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'SaeedLab/BindScreen-lora-LIT_PCBA'
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).eval()
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