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