Datasets:

little1d
/

mol_edit_data

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. 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
ArXiv https://arxiv.org/abs/2512.20135

If you use MolAct in your research, please cite:

@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}
}