  Molecule discovery serves as a cornerstone in numerous scientific domains,
fueling the development of new materials and innovative drug designs. Recent
developments of in-silico molecule discovery have highlighted the promising
results of cross-modal techniques, which bridge molecular structures with their
descriptive annotations. However, these cross-modal methods frequently
encounter the issue of data scarcity, hampering their performance and
application. In this paper, we address the low-resource challenge by utilizing
artificially-real data generated by Large Language Models (LLMs). We first
introduce a retrieval-based prompting strategy to construct high-quality pseudo
data, then explore the optimal method to effectively leverage this pseudo data.
Experiments show that using pseudo data for domain adaptation outperforms all
existing methods, while also requiring a smaller model scale, reduced data size
and lower training cost, highlighting its efficiency. Furthermore, our method
shows a sustained improvement as the volume of pseudo data increases, revealing
the great potential of pseudo data in advancing low-resource cross-modal
molecule discovery. Our code and data are available at
https://github.com/SCIR-HI/ArtificiallyR2R.

Computation and Language