| # SMILES-based Transformer Encoder-Decoder (SMI-TED) | |
| [](https://arxiv.org/abs/2407.20267) | |
| This repository provides a HuggingFace-compatible version of the SMI-TED model, a SMILES-based Transformer Encoder-Decoder for chemical language modeling. | |
| --- | |
| ## 📦 Forked Resources | |
| - **Forked GitHub:** [bisect-group/materials-smi-ted-fork](https://github.com/bisect-group/materials-smi-ted-fork) | |
| - **Forked HuggingFace:** [bisectgroup/materials-smi-ted-fork](https://huggingface.co/bisectgroup/materials-smi-ted-fork) | |
| ## 🏷️ Original Resources | |
| - **Original GitHub:** [IBM/materials (smi_ted)](https://github.com/IBM/materials/tree/main/models/smi_ted) | |
| - **Original HuggingFace:** [ibm/materials.smi-ted](https://huggingface.co/ibm/materials.smi-ted) | |
| - **Publication:** [A Large Encoder-Decoder Family of Foundation Models for Chemical Language](https://arxiv.org/abs/2407.20267) | |
| --- | |
| ## 🚀 Usage | |
| ```bash | |
| pip install smi-ted | |
| ``` | |
| ```python | |
| import torch | |
| import smi_ted | |
| from transformers import AutoConfig, AutoModel, AutoTokenizer | |
| # Load config, tokenizer, and model from HuggingFace Hub | |
| config = AutoConfig.from_pretrained("bisectgroup/materials-smi-ted-fork") | |
| tokenizer = AutoTokenizer.from_pretrained("bisectgroup/materials-smi-ted-fork") | |
| model = AutoModel.from_pretrained("bisectgroup/materials-smi-ted-fork") | |
| # Link tokenizer to model (required for SMILES reconstruction) | |
| model.smi_ted.tokenizer = tokenizer | |
| model.smi_ted.set_padding_idx_from_tokenizer() | |
| # Example SMILES strings | |
| smiles = [ | |
| 'CC1C2CCC(C2)C1CN(CCO)C(=O)c1ccc(Cl)cc1', | |
| 'COc1ccc(-c2cc(=O)c3c(O)c(OC)c(OC)cc3o2)cc1O', | |
| 'CCOC(=O)c1ncn2c1CN(C)C(=O)c1cc(F)ccc1-2', | |
| 'Clc1ccccc1-c1nc(-c2ccncc2)no1', | |
| 'CC(C)(Oc1ccc(Cl)cc1)C(=O)OCc1cccc(CO)n1' | |
| ] | |
| # Encode and decode SMILES | |
| with torch.no_grad(): | |
| encoder_outputs = model.encode(smiles) | |
| decoded_smiles = model.decode(encoder_outputs) | |
| print(decoded_smiles) | |
| ``` | |
| --- | |
| ## 📝 Citation | |
| If you use this model, please cite: | |
| ```bibtex | |
| @article{soares2025open, | |
| title={An open-source family of large encoder-decoder foundation models for chemistry}, | |
| author={Soares, Eduardo and Vital Brazil, Emilio and Shirasuna, Victor and Zubarev, Dmitry and Cerqueira, Renato and Schmidt, Kristin}, | |
| journal={Communications Chemistry}, | |
| volume={8}, | |
| number={1}, | |
| pages={193}, | |
| year={2025}, | |
| publisher={Nature Publishing Group UK London} | |
| } | |
| @article{soares2024large, | |
| title={A large encoder-decoder family of foundation models for chemical language}, | |
| author={Soares, Eduardo and Shirasuna, Victor and Brazil, Emilio Vital and Cerqueira, Renato and Zubarev, Dmitry and Schmidt, Kristin}, | |
| journal={arXiv preprint arXiv:2407.20267}, | |
| year={2024} | |
| } | |
| ``` | |
| --- | |
| ## 📧 Contact | |
| For questions or collaborations, contact: | |
| - eduardo.soares@ibm.com | |
| - evital@br.ibm.com | |
| --- | |
| **Note:** | |
| This fork adapts the original SMI-TED codebase for seamless integration with HuggingFace's AutoModel and AutoTokenizer interfaces. For full source code and training scripts, see the [original IBM repo](https://github.com/IBM/materials/tree/main/models/smi_ted). |