|
|
--- |
|
|
task_categories: |
|
|
- text-generation |
|
|
- chemistry |
|
|
language: |
|
|
- en |
|
|
tags: |
|
|
- molecule-design |
|
|
- molecular-editing |
|
|
- agentic-rl |
|
|
- drug-discovery |
|
|
pretty_name: MolAct-Instruct |
|
|
--- |
|
|
|
|
|
# MolAct-Instruct Dataset |
|
|
|
|
|
This dataset is used to train **MolAct**, an Agentic RL framework for molecular editing and optimization. |
|
|
|
|
|
### Description |
|
|
The dataset is derived from [ChemCoTBench](https://huggingface.co/datasets/OpenMol/ChemCoTBench). We extracted the source molecules (SMILES) and task specifications (editing instructions or optimization objectives) while removing the intermediate Chain-of-Thought (CoT) reasoning steps to fit the **Reinforcement Learning** environment. |
|
|
|
|
|
- **Stage 1 (Editing):** Focuses on functional group addition, deletion, and substitution. |
|
|
- Stage 2 (Optimization): Focuses on multi-objective property optimization (LogP, Solubility, QED, bioactivity targets). |
|
|
|
|
|
### Reference |
|
|
For more details on the framework and training paradigm, please visit our GitHub repository. |
|
|
- **GitHub:** [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} |
|
|
} |
|
|
``` |
