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license: creativeml-openrail-m |
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base_model: |
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- facebook/esm2_t30_150M_UR50D |
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--- |
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[] |
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(https://colab.research.google.com/drive/1OoX9zDwdSD88UGXxlFctnq_UPcnkkWdp?usp=sharing) |
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**PIPES-M**, a deep learning-based binary classifier designed to predict protease inhibitor (PI) activity from primary protein sequences. |
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PIPES-M is a fine-tuned sequence classification model built on the **ESM-2** protein language model (EsmForSequenceClassification): |
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- Base model: `facebook/esm2_t30_150M_UR50D` (150 million parameters, 30 layers) |
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- Pre-trained on UniRef50 via masked language modeling |
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Fine-tuning was performed on a high-quality curated dataset comprising: |
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- Positive examples: known protease inhibitors (<250 AA) from the MEROPS and Uniprot database |
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- Negative examples: non-inhibitors selected from UniProt using sequence similarity and Pfam domain analysis |
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Training used sequence-only input, requiring no structural data. The classification head leverages evolutionary and physicochemical features encoded by ESM-2. |
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Maximum sequence length is fixed at 250 residues; longer sequences are truncated after 250 AA from the N-terminus, appropriate for the typical size range of small secreted inhibitors. |
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license: creativeml-openrail-m |
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