Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery

Xiaochu Tong, Ning Qu, Xiangtai Kong, Shengkun Ni, Jingyi Zhou, Kun Wang, Lehan Zhang, Yiming Wen, Jiangshan Shi, Sulin Zhang (), Xutong Li () and Mingyue Zheng ()
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Xiaochu Tong: Chinese Academy of Sciences
Ning Qu: Chinese Academy of Sciences
Xiangtai Kong: Chinese Academy of Sciences
Shengkun Ni: Chinese Academy of Sciences
Jingyi Zhou: Chinese Academy of Sciences
Kun Wang: Chinese Academy of Sciences
Lehan Zhang: Chinese Academy of Sciences
Yiming Wen: Chinese Academy of Sciences
Jiangshan Shi: Chinese Academy of Sciences
Sulin Zhang: Chinese Academy of Sciences
Xutong Li: Chinese Academy of Sciences
Mingyue Zheng: Chinese Academy of Sciences

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract Artificial intelligence transforms drug discovery, with phenotype-based approaches emerging as a promising alternative to target-based methods, overcoming limitations like lack of well-defined targets. While chemical-induced transcriptional profiles offer a comprehensive view of drug mechanisms, inherent noise often obscures the true signal, hindering their potential for meaningful insights. Here, we highlight the development of TranSiGen, a deep generative model employing self-supervised representation learning. TranSiGen analyzes basal cell gene expression and molecular structures to reconstruct chemical-induced transcriptional profiles with high accuracy. By capturing both cellular and compound information, TranSiGen-derived representations demonstrate efficacy in diverse downstream tasks like ligand-based virtual screening, drug response prediction, and phenotype-based drug repurposing. Notably, in vitro validation of TranSiGen’s application in pancreatic cancer drug discovery highlights its potential for identifying effective compounds. We envisage that integrating TranSiGen into the drug discovery and mechanism research holds significant promise for advancing biomedicine.

Date: 2024
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DOI: 10.1038/s41467-024-49620-3

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