Suiren-ConfAvg

<div align="center" style="line-height:1"> <img src="./gewu.png" alt="logo" width="20%" /> <a href="https://github.com/golab-ai/Suiren-Property-Prediction" target="_blank"><img alt="github" src="https://img.shields.io/badge/Github-Gewu-blue?logo=github"/></a> <a href="https://github.com/golab-ai/Huntianling"><img alt="Homepage" src="https://img.shields.io/badge/🤖Skills-Huntianling-blue"/></a> <a href="https://drive.google.com/file/d/1vUMYzhmhCeNU18WE5D_xV4gQWxfU7kI7/view?usp=sharing"><img alt="slides" src="https://img.shields.io/badge/Slides-Suiren-white?logo=slideshare"/></a> </div> <div align="center" style="line-height: 1;"> <a href="https://huggingface.co/ajy112/Suiren-Base/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/badge/License-Modified_MIT-f5de53?&color=f5de53"/></a> </div>

Suiren-ConfAvg

Suiren-ConfAvg is derived from the Suiren-Base model through distillation, designed to characterize the conformational average representations of molecules. In short, Suiren-Base can provide microscopic representations of various molecular conformations, but many scientific tasks rely on the ensemble averaging of multiple conformations. We compressed the features of Suiren-Base into Suiren-ConfAvg through a special distillation method, whose representations can be used to solve some macroscopic tasks, such as property prediction and molecular generation. We provide a quick training script for property prediction on GitHub.

<div align="center"> <img src="./suiren-family.jpg" alt="main_flowchart" width="100%" /> </div>

Usage

You can use the scripts we provide in github to train your data directly. Alternatively, if you want to use the Suiren-ConfAvg model directly, you can call the GNN class in the file. The configuration for Suiren-ConfAvg is:

model = GNN(
            num_layer=12,
            emb_dim=256,
            drop_ratio=0.0,
            model_mode="pretrain",
            output_type="last" # "last" means returning the node embedding of the last layer (torch.tensor); "layers" means returning the node embedding of the all layers (list(torch.tensor))
        )
        

Citation

If you use Suiren models, please cite the relevant papers for the underlying models.

@article{an2026suiren,
  title={Suiren-1.0 Technical Report: A Family of Molecular Foundation Models},
  author={An, Junyi and Lu, Xinyu and Shi, Yun-Fei and Xu, Li-Cheng and Zhang, Nannan and Qu, Chao and Qi, Yuan and Cao, Fenglei},
  journal={arXiv preprint arXiv:2603.21942},
  year={2026}
}