PseudoMD-1M

Pre-training dataset used in paper "From Artificially Real to Real: Leveraging Pseudo Data from Large Language Models for Low-Resource Molecule Discovery" (AAAI 2024)

PseudoMD-1M dataset is the first artificially-real dataset for cross-modal molecule discovery, which consists of 1,020,139 pseudo molecule-description pairs. Every molecule is represented using its Canonical SMILES notation, sourced from PubChem via the PUG View API. On average, each description within PseudoMD-1M contains 5.11 sentences, 106.47 words, and 165.07 tokens.

Citation

If you found the dataset useful, please cite:

@article{chen2023artificially,
  title={From Artificially Real to Real: Leveraging Pseudo Data from Large Language Models for Low-Resource Molecule Discovery},
  author={Chen, Yuhan and Xi, Nuwa and Du, Yanrui and Wang, Haochun and Jianyu, Chen and Zhao, Sendong and Qin, Bing},
  journal={arXiv preprint arXiv:2309.05203},
  year={2023}
}