Drug target inference by mining transcriptional data using a novel graph convolutional network framework

• Published in: Protein & cell Vol. 13; no. 4; pp. 281 - 301
• Main Authors: Zhong, Feisheng, Wu, Xiaolong, Yang, Ruirui, Li, Xutong, Wang, Dingyan, Fu, Zunyun, Liu, Xiaohong, Wan, XiaoZhe, Yang, Tianbiao, Fan, Zisheng, Zhang, Yinghui, Luo, Xiaomin, Chen, Kaixian, Zhang, Sulin, Jiang, Hualiang, Zheng, Mingyue
• Format: Journal Article
• Language: English
• Published: Beijing Higher Education Press 01.04.2022; Springer Nature B.V
• Subjects: Biochemistry; Biomedical and Life Sciences; Cell Biology; Chemical compounds; deep learning; Developmental Biology; Drug Delivery Systems; drug target inference; experimental verification; Human Genetics; Life Sciences; Protein Science; Proteins; Research Article; Stem Cells; Therapeutic targets; Transcription; Transcriptome; Transcriptomics; transcriptomics; deep learning; drug target inference; experimental verification
• ISSN: 1674-800X; 1674-8018; 1674-8018
• DOI: 10.1007/s13238-021-00885-0

A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or offtarget effects. Recently, the fast accumulation of gene transcriptional profiling data provides us an unprecedented opportunity to explore the protein targets of chemical compounds from the perspective of cell transcriptomics and RNA biology. Here, we propose a novel Siamese spectral-based graph convolutional network (SSGCN) model for inferring the protein targets of chemical compounds from gene transcriptional profiles. Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets, and the biological networks under different experiment conditions further complicate the situation, the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles. On a benchmark set and a large time-split validation dataset, the model achieved higher target inference accuracy as compared to previous methods such as Connectivity Map. Further experimental validations of prediction results highlight the practical usefulness of SSGCN in either inferring the interacting targets of compound, or reversely, in finding novel inhibitors of a given target of interest.