A Deep Generative Model For Deciphering Cellular Dynamics And In Silico Drug Discovery In Complex Diseases Yumin Zheng Jonas C Schupp Taylor Adams Geremy Clair Aurelien Justet Farida Ahangari Xiting Yan Paul Hansen Marianne Carlon Emanuela Cortesi Marie Vermant Robin Vos Laurens J Sadeleer Ivan O Rosas Ricardo Pineda John by Yumin Zheng & Jonas C. Schupp & Taylor Adams & Geremy Clair & Aurelien Justet & Farida Ahangari & Xiting Yan & Paul Hansen & Marianne Carlon & Emanuela Cortesi & Marie Vermant & Robin Vos & Laurens J. Sadeleer & Ivan O. Rosas & Ricardo Pineda & John... instant download after payment.

Human diseases are characterized by intricate cellular dynamics. Single-cell transcriptomics provides critical insights, yet a persistent gap remains in computational tools for detailed disease progression analysis and targeted in silico drug interventions. Here we introduce UNAGI, a deep generative neural network tailored to analyse time-series single-cell transcriptomic data. This tool captures the complex cellular dynamics underlying disease progression, enhancing drug perturbation modelling and screening. When applied to a dataset from patients with idiopathic pulmonary fbrosis, UNAGI learns disease-informed cell embeddings that sharpen our understanding of disease progression, leading to the identifcation of potential therapeutic drug candidates. Validation using proteomics reveals the accuracy of UNAGI’s cellular dynamics analysis, and the use of the fbrotic cocktail-treated human precision-cut lung slices confrms UNAGI’s predictions that nifedipine, an antihypertensive drug, may have anti-fbrotic efects on human tissues. UNAGI’s versatility extends to other diseases, including COVID, demonstrating adaptability and confrming its broader applicability in decoding complex cellular dynamics beyond idiopathic pulmonary fbrosis, amplifying its use in the quest for therapeutic solutions across diverse pathological landscapes.