Paecilomyces tenuipes (P. tenuipes) is a fungus cultivated artificially by South Korean researchers, utilizing rice bran as its substrate. The increased demand for this fungus has not been met with successful cultivation methods for fruiting body production in natural environments. Therefore, we tested the effect on the growth of P. tenuipes using a Solid media based on pests. In this results, the Solid media based on M.alternatus was effective in increasing the growth of P. tenuipes and the content of cordycepin. Moreover, we confirmed the conditions for manufacturing a Solid media based on M.alternatus for P. tenuipes growth. We suggested that the growth-promoting compounds offers valuable insights for optimizing fungal cultivation conditions, thereby enhancing productivity and contributing to a broader understanding of fungal physiology in varying nutritional environments.

Plants synthesize antioxidant compounds as a defense mechanism against reactive oxygen species. Recently, plant-derived antioxidant compounds have attracted attention due to the increasing consumer awareness in the heath industry. However, traditional methods for measuring the antioxidant activity of these compounds are time-consuming and costly. Therefore, our study constructed a quantitative structure-activity relationship (QSAR) model that can predict antioxidant activity using graph convolutional networks (GCN) from plant structural data. The accuracy (Acc) of the model reached 0.6 and the loss reached 0.03. Although with lower accuracy than previously reported QSAR models, our model showed the possibility of predicting DPPH antioxidant activity in a wide range of plant compounds (phenolics, polyphenols, vitamins, etc.) based on their graph structure.