Computational Prediction of the Phenotypic Effect of Flavonoids on Adiponectin Biosynthesis

In silico machine learning applications
for phenotype-based
screening have primarily been limited due to the lack of machine-readable
data related to disease phenotypes. Adiponectin, a nuclear receptor
(NR)-regulated adipocytokine, is relatively downregulated in human
metabolic diseases. Here, we present a machine-learning model to predict
the adiponectin-secretion-promoting activity of flavonoid-associated
phytochemicals (FAPs). We modeled a structure–activity relationship
between the chemical similarity of FAPs and their bioactivities using
a random forest-based classifier, which provided the NR activity of
each FAP as a probability. To link the classifier-predicted NR activity
to the phenotype, we next designed a single-cell transcriptomics-based
multiple linear regression model to generate the relative adiponectin
score (RAS) of FAPs. In experimental validation, estimated RAS values
of FAPs isolated from Scutellaria baicalensis exhibited a significant correlation with their adiponectin-secretion-promoting
activity. The combined cheminformatics and bioinformatics approach
enables the computational reconstruction of phenotype-based screening
systems.