|
|
--- |
|
|
license: apache-2.0 |
|
|
tags: |
|
|
- chemistry |
|
|
- drug-discovery |
|
|
- molecular-modeling |
|
|
- mumo |
|
|
pipeline_tag: graph-ml |
|
|
library_name: transformers |
|
|
--- |
|
|
|
|
|
# mumo-pretrain |
|
|
|
|
|
This model was trained using MuMo (Multi-Modal Molecular) framework, as presented in the paper [Structure-Aware Fusion with Progressive Injection for Multimodal Molecular Representation Learning](https://huggingface.co/papers/2510.23640). |
|
|
The official code repository is available at: https://github.com/selmiss/MuMo |
|
|
|
|
|
## Model Description |
|
|
|
|
|
- **Model Type**: MuMo Pretrained Model |
|
|
- **Training Data**: Molecular structures and properties |
|
|
- **Framework**: PyTorch + Transformers |
|
|
|
|
|
## Usage |
|
|
|
|
|
```python |
|
|
from transformers import AutoConfig, AutoTokenizer, AutoModel |
|
|
|
|
|
# Load model |
|
|
model_path = "zihaojing/mumo-pretrain" |
|
|
config = AutoConfig.from_pretrained(model_path, trust_remote_code=True) |
|
|
tokenizer = AutoTokenizer.from_pretrained(model_path) |
|
|
model = AutoModel.from_pretrained(model_path, trust_remote_code=True) |
|
|
|
|
|
# Example usage |
|
|
smiles = "CCO" # Ethanol |
|
|
inputs = tokenizer(smiles, return_tensors="pt") |
|
|
outputs = model(**inputs) |
|
|
``` |
|
|
|
|
|
## Training Details |
|
|
|
|
|
- Training script: See the [official GitHub repository](https://github.com/selmiss/MuMo) for details. |
|
|
- Framework: Transformers + DeepSpeed |
|
|
|
|
|
## Citation |
|
|
|
|
|
If you use this model or the MuMo framework, please cite our paper: |
|
|
|
|
|
```bibtex |
|
|
@inproceedings{jing2025mumo, |
|
|
title = {MuMo: Multimodal Molecular Representation Learning via Structural Fusion and Progressive Injection}, |
|
|
author = {Jing, Zihao and Sun, Yan and Li, Yan Yi and Janarthanan, Sugitha and Deng, Alana and Hu, Pingzhao}, |
|
|
booktitle = {Advances in Neural Information Processing Systems (NeurIPS)}, |
|
|
year = {2025} |
|
|
} |
|
|
``` |
