|
|
--- |
|
|
language: |
|
|
- en |
|
|
library_name: transformers |
|
|
tags: |
|
|
- chemistry |
|
|
- molecule-optimization |
|
|
- agentic-rl |
|
|
- grpo |
|
|
license: apache-2.0 |
|
|
datasets: |
|
|
- little1d/mol_opt_data |
|
|
base_model: |
|
|
- little1d/MolEditAgent-7B |
|
|
--- |
|
|
|
|
|
# MolOptAgent-{3B/7B} |
|
|
|
|
|
**MolOptAgent** is the Stage-2 model of the **MolAct** framework, continued training from MolEditAgent using **Agentic Reinforcement Learning (GRPO)**. |
|
|
|
|
|
### Key Features |
|
|
- **Objective:** Optimized for multi-step molecular property optimization (e.g., LogP, Solubility, QED, Bioactivity). |
|
|
- **Zero-Tolerance for Errors:** Guided by real-time tool feedback, it minimizes "Chemical Hallucinations" and ensures nearly 100% molecular validity. |
|
|
- **Performance:** Outperforms strong reasoning models like Claude-3.7 and DeepSeek-R1 in complex property-guided editing tasks. |
|
|
|
|
|
### Links |
|
|
- **GitHub Repository:** [https://github.com/little1d/MolAct](https://github.com/little1d/MolAct) |
|
|
- **ArXiv** [https://arxiv.org/abs/2512.20135](https://arxiv.org/abs/2512.20135) |
|
|
|
|
|
|
|
|
If you use MolAct in your research, please cite: |
|
|
|
|
|
```bibtex |
|
|
@article{molact2025, |
|
|
title={MolAct: An Agentic RL Framework for Molecular Editing and Property Optimization}, |
|
|
author={Zhuo Yang and Yeyun Chen and Jiaqing Xie and Ben Gao and Shuaike Shen and Wanhao Liu and Liujia Yang and Beilun Wang and Tianfan Fu and Yuqiang Li}, |
|
|
year={2025}, |
|
|
eprint={2512.20135}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.AI}, |
|
|
url={https://arxiv.org/abs/2512.20135} |
|
|
} |
|
|
``` |
