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README.md
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Unlike traditional protein design models, GPepT is trained in a self-supervised manner, using raw sequence data without explicit annotation. This design enables the model to generalize across diverse sequence spaces, producing functional antimicrobial peptidomimetics upon fine-tuning.
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## **Using GPepT for Sequence Generation**
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The model excels at generating peptidomimetic sequences in a zero-shot fashion, but it can also be fine-tuned on custom datasets to generate sequences tailored to specific requirements.
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### **Example 1: Zero-Shot Sequence Generation**
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GPepT generates sequences that extend from a specified input token (e.g., `<|endoftext|>`). If no input is provided, it selects the start token automatically and generates likely sequences. Here’s a Python example:
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Unlike traditional protein design models, GPepT is trained in a self-supervised manner, using raw sequence data without explicit annotation. This design enables the model to generalize across diverse sequence spaces, producing functional antimicrobial peptidomimetics upon fine-tuning.
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SMILES representation, and selected chemical properties of each token, which corresponds to a non-canonical amino acid or terminal modification.
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---
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## **Using GPepT for Sequence Generation**
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The model excels at generating peptidomimetic sequences in a zero-shot fashion, but it can also be fine-tuned on custom datasets to generate sequences tailored to specific requirements.
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### **Example 1: Zero-Shot Sequence Generation**
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GPepT generates sequences that extend from a specified input token (e.g., `<|endoftext|>`). If no input is provided, it selects the start token automatically and generates likely sequences. Here’s a Python example:
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