This research introduces
UNAGI, a deep generative model designed to analyze
cellular dynamics and facilitate
in silico drug discovery for complex diseases like
idiopathic pulmonary fibrosis and
COVID-19. By combining a
graph VAE-GAN architecture with
gene regulatory network inference, the tool maps how cell populations transition across different stages of a disease.
UNAGI distinguishes itself from previous methods by using an
iterative training strategy that emphasizes disease-specific markers, improving the precision of
cell embeddings. The model enables researchers to perform
simulated perturbations, virtually testing thousands of compounds to identify those that might shift diseased cells back toward a
healthy state. Validated through
precision-cut lung slices, this framework identifies potential therapeutic candidates, such as
nifedipine, without requiring prior experimental drug-response data. Ultimately,
UNAGI offers a scalable, unsupervised solution for decoding the temporal complexity of
multifactorial disorders.
References:
- Zheng Y, Schupp J C, Adams T, et al. A deep generative model for deciphering cellular dynamics and in silico drug discovery in complex diseases[J]. Nature Biomedical Engineering, 2025: 1-26.