| --- |
| language: en |
| license: mit |
| datasets: |
| - custom |
| tags: |
| - molt5 |
| - drug-drug-interaction |
| - biomedical |
| library_name: transformers |
| pipeline_tag: text-generation |
| --- |
| |
|
|
| # MolT5 for Drug–Drug Interaction Prediction |
|
|
| This repository contains a MolT5 model fine-tuned for Drug–Drug Interaction (DDI) prediction. |
| It is designed to infer potential interactions between drugs given their SMILES strings or textual descriptions. |
|
|
| ## Model Description |
| MolT5 is a T5-based architecture designed for molecular tasks. |
| This model was further fine-tuned on a custom drug–drug interaction dataset to generate interaction classes or descriptions. |
|
|
| ### Files Included |
| - `config.json`: model configuration |
| - `model.safetensors`: model weights |
| - `tokenizer_config.json`, `special_tokens_map.json`, `spiece.model`: tokenizer files |
| - `generation_config.json`: decoding parameters |
| - `added_tokens.json`: extra tokens |
|
|
| ## Example Usage |
|
|
| ```python |
| from transformers import T5ForConditionalGeneration, T5Tokenizer |
| |
| tokenizer = T5Tokenizer.from_pretrained("acdsd/DDI") |
| model = T5ForConditionalGeneration.from_pretrained("acdsd/DDI") |
| |
| query = "[DRUG1] ibuprofen SMILES CC(C)CC1=CC=C(C=C1)C(C)C(=O)O [DRUG2] paracetamol SMILES CC(=O)NC1=CC=C(O)C=C1" |
| |
| inputs = tokenizer(query, return_tensors="pt") |
| outputs = model.generate(**inputs, num_beams=4, max_length=128) |
| print(tokenizer.decode(outputs, skip_special_tokens=True)) |
| ``` |
|
|
|
|
| ## Intended Use |
| - Drug–Drug Interaction classification |
| - Drug safety/toxicity assessment |
|
|
| ## License |
| MIT License |
|
|
| ## Citation |
| <!-- |
| If you use this model: |
| @model{your_org_molt5_ddi_2025, |
| title={MolT5 Fine-tuned for Drug–Drug Interaction Prediction}, |
| year={2025}, |
| author={Your Name}, |
| url={https://huggingface.co/acdsd/DDI} |
| } --> |
