Molecular Graph-Based Deep Learning Algorithm Facilitates an Imaging-Based Strategy for Rapid Discovery of Small Molecules Modulating Biomolecular Condensates

Biomolecular
condensates are proposed to cause diseases, such as
cancer and neurodegeneration, by concentrating proteins at abnormal
subcellular loci. Imaging-based compound screens have been used to
identify small molecules that reverse or promote biomolecular condensates.
However, limitations of conventional imaging-based methods restrict
the screening scale. Here, we used a graph convolutional network (GCN)-based
computational approach and identified small molecule candidates that
reduce the nuclear liquid–liquid phase separation of TAR DNA-binding
protein 43 (TDP-43), an essential protein that undergoes phase transition
in neurodegenerative diseases. We demonstrated that the GCN-based
deep learning algorithm is suitable for spatial information extraction
from the molecular graph. Thus, this is a promising method to identify
small molecule candidates with novel scaffolds. Furthermore, we validated
that these candidates do not affect the normal splicing function of
TDP-43. Taken together, a combination of an imaging-based screen and
a GCN-based deep learning method dramatically improves the speed and
accuracy of the compound screen for biomolecular condensates.