---
title: Suiren-Model-Set
canonical_url: "https://www.modelscope.cn/models/ajy112/Suiren-Model-Set"
md_url: "https://www.modelscope.cn/models/ajy112/Suiren-Model-Set.md"
repository: ajy112/Suiren-Model-Set
chinese_name: Suiren-Model-Set
last_updated: 2026-07-20
license: mit
library_name:
  - pytorch
frameworks:
  - Pytorch
language:
  - en
downloads: 177
stars: 0
tags:
  - chemistry
  - molecular-foundation-model
  - quantum-chemistry
  - equivariant-neural-networks
---

# Suiren-Model-Set

> Suiren-Model-Set - ajy112 在 ModelScope 开源的模型。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…

ajy112/Suiren-Model-Set 是 ModelScope 魔搭社区上的机器学习模型，采用 mit 许可。

- **Repository**: ajy112/Suiren-Model-Set
- **License**: mit
- **Tags**: chemistry, molecular-foundation-model, quantum-chemistry, equivariant-neural-networks
- **Downloads**: 177
- **Stars**: 0
- **Last updated**: 2026-07-20

Source: https://www.modelscope.cn/models/ajy112/Suiren-Model-Set

---

<div align="center" style="line-height:1">
  <img src="./images/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](https://huggingface.co/ajy112/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](https://github.com/golab-ai/Suiren-Property-Prediction).


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

## Usage

You can use the scripts we provide in [github](https://github.com/golab-ai/Suiren-Property-Prediction) to train your data directly.

## 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}
}
```