Molecular discovery · Molecular property → structure
MolP2SBench
Recover a masked molecular fragment using computed physical properties. Known structures provide reference answers; controlled views help investigate changes in predictions.
Teaching starter
2 observations · 2 candidates · 1 molecular family
Includes model inputs, separate labels, a scorer and a Python example. Python 3.9+; no API key needed for the smoke test.
Full benchmark
136 source problems per property channel · 2–4 candidates
Use the original repository for the complete task views, labels, prompts and scorer. Its format differs from the teaching starter.
View original repositoryFull-data instructions
External source. Check the repository and underlying data’s reuse terms.
- Structure recovery
- Evidence use
- Counterfactual response
Capabilities examined in the full benchmark. The teaching pair introduces the interface.
What do these counts mean?
A task is one input record to answer. A family groups a source recovery problem with its related observations. A property channel supplies a kind of evidence, such as dipole magnitude. An evidence condition specifies how it is paired with the task: correct, shuffled or counterfactual. Keep related records together when building independent evaluation splits.
- Input
- Masked structure, property values and candidate completions
- Output
- A candidate choice for each observation
- Feedback
- Correctness and reference comparisons on development cases
Source and citation
Benchmark paper. Yuhang Xie, Zhiyi Shi, Zeyi Zhang, Zhisheng Qi, Yushun Dong, Yu Wang, Xiongye Xiao. MolP2SBench: Do LLMs Really Reason from Molecular Properties to Structure? arXiv:2610.12522 (2026).
Cite this resource
Cite the benchmark paper for MolP2SBench tasks and design. For QM9S-derived records, also cite the underlying QM9S paper and identify the dataset release used. Each source has its own reuse terms. A deep learning model for predicting selected organic molecular spectra · QM9S dataset release.
Select and copy the entries below, or download the BibTeX file above.
@misc{xie2026molp2sbench,
title = {MolP2SBench: Do LLMs Really Reason from Molecular Properties to Structure?},
author = {Yuhang Xie and Zhiyi Shi and Zeyi Zhang and Zhisheng Qi and Yushun Dong and Yu Wang and Xiongye Xiao},
year = {2026},
eprint = {2610.12522},
archivePrefix = {arXiv},
primaryClass = {physics.chem-ph},
url = {https://arxiv.org/abs/2610.12522}
}
@article{zou2023molecularspectra,
title = {A deep learning model for predicting selected organic molecular spectra},
author = {Zihan Zou and Yujin Zhang and Lijun Liang and Mingzhi Wei and Jiancai Leng and Jun Jiang and Yi Luo and Wei Hu},
journal = {Nature Computational Science},
year = {2023},
volume = {3},
pages = {957--964},
doi = {10.1038/s43588-023-00550-y},
url = {https://doi.org/10.1038/s43588-023-00550-y}
}