Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery

Xiaoning Qi, Lianhe Zhao, Chenyu Tian, Yueyue Li, Zhen-Lin Chen, Peipei Huo, Runsheng Chen, Xiaodong Liu, Baoping Wan, Shengyong Yang () and Yi Zhao ()
Additional contact information
Xiaoning Qi: Institute of Computing Technology, Chinese Academy of Sciences
Lianhe Zhao: Institute of Computing Technology, Chinese Academy of Sciences
Chenyu Tian: Sichuan University
Yueyue Li: Sichuan University
Zhen-Lin Chen: University of Chinese Academy of Sciences
Peipei Huo: Luoyang Institute of Information Technology Industries
Runsheng Chen: Sichuan University
Xiaodong Liu: University of Chinese Academy Sciences
Baoping Wan: Institute of Computing Technology, Chinese Academy of Sciences
Shengyong Yang: Sichuan University
Yi Zhao: Institute of Computing Technology, Chinese Academy of Sciences

Nature Communications, 2024, vol. 15, issue 1, 1-19

Abstract: Abstract Understanding transcriptional responses to chemical perturbations is central to drug discovery, but exhaustive experimental screening of disease-compound combinations is unfeasible. To overcome this limitation, here we introduce PRnet, a perturbation-conditioned deep generative model that predicts transcriptional responses to novel chemical perturbations that have never experimentally perturbed at bulk and single-cell levels. Evaluations indicate that PRnet outperforms alternative methods in predicting responses across novel compounds, pathways, and cell lines. PRnet enables gene-level response interpretation and in-silico drug screening for diseases based on gene signatures. PRnet further identifies and experimentally validates novel compound candidates against small cell lung cancer and colorectal cancer. Lastly, PRnet generates a large-scale integration atlas of perturbation profiles, covering 88 cell lines, 52 tissues, and various compound libraries. PRnet provides a robust and scalable candidate recommendation workflow and successfully recommends drug candidates for 233 diseases. Overall, PRnet is an effective and valuable tool for gene-based therapeutics screening.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-53457-1 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-53457-1

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-53457-1

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().