metadata
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. 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
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 for details.
- Framework: Transformers + DeepSpeed
Citation
If you use this model or the MuMo framework, please cite our paper:
@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}
}