---
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}
}
```