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--- |
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tags: |
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- protein |
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- ibm |
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- mammal |
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- pytorch |
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- transformers |
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library_name: biomed |
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license: apache-2.0 |
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--- |
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Protein solubility is a critical factor in both pharmaceutical research and production processes, as it can significantly impact the quality and function of a protein. |
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This is an example for finetuning `ibm/biomed.omics.bl.sm-ted-400m` for protein solubility prediction (binary classification) based solely on the amino acid sequence. |
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The benchmark defined in: https://academic.oup.com/bioinformatics/article/34/15/2605/4938490 |
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Data retrieved from: https://zenodo.org/records/1162886 |
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## Model Summary |
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- **Developers:** IBM Research |
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- **GitHub Repository:** https://github.com/BiomedSciAI/biomed-multi-alignment |
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- **Paper:** TBD |
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- **Release Date**: Oct 28th, 2024 |
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- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0). |
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## Usage |
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Using `ibm/biomed.omics.bl.sm.ma-ted-400m` requires installing [https://github.com/BiomedSciAI/biomed-multi-alignment](https://github.com/TBD) |
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``` |
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pip install git+https://github.com/BiomedSciAI/biomed-multi-alignment.git |
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``` |
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A simple example for a task already supported by `ibm/biomed.omics.bl.sm.ma-ted-400m`: |
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```python |
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import os |
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from fuse.data.tokenizers.modular_tokenizer.op import ModularTokenizerOp |
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from mammal.examples.protein_solubility.task import ProteinSolubilityTask |
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from mammal.keys import CLS_PRED, SCORES |
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from mammal.model import Mammal |
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# Load Model |
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model = Mammal.from_pretrained("ibm/biomed.omics.bl.sm.ma-ted-400m.protein_solubility") |
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# Load Tokenizer |
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tokenizer_op = ModularTokenizerOp.from_pretrained("ibm/biomed.omics.bl.sm.ma-ted-400m.protein_solubility") |
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# convert to MAMMAL style |
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sample_dict = {"protein_seq": protein_seq} |
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sample_dict = ProteinSolubilityTask.data_preprocessing( |
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sample_dict=sample_dict, |
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protein_sequence_key="protein_seq", |
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tokenizer_op=tokenizer_op, |
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device=model.device, |
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) |
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# running in generate mode |
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batch_dict = model.generate( |
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[sample_dict], |
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output_scores=True, |
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return_dict_in_generate=True, |
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max_new_tokens=5, |
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) |
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# Post-process the model's output |
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ans = ProteinSolubilityTask.process_model_output( |
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tokenizer_op=tokenizer_op, |
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decoder_output=batch_dict[CLS_PRED][0], |
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decoder_output_scores=batch_dict[SCORES][0], |
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) |
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# Print prediction |
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print(f"{ans=}") |
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``` |
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For more advanced usage, see our detailed example at: on `https://github.com/BiomedSciAI/biomed-multi-alignment` |
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## Citation |
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If you found our work useful, please consider giving a star to the repo and cite our paper: |
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``` |
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@article{TBD, |
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title={TBD}, |
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author={IBM Research Team}, |
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jounal={arXiv preprint arXiv:TBD}, |
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year={2024} |
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} |
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``` |
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