Drug target inference by mining transcriptional data using a novel graph convolutional network framework.
10.1007/s13238-021-00885-0
- Author:
Feisheng ZHONG
1
;
Xiaolong WU
1
;
Ruirui YANG
1
;
Xutong LI
1
;
Dingyan WANG
1
;
Zunyun FU
1
;
Xiaohong LIU
1
;
XiaoZhe WAN
1
;
Tianbiao YANG
1
;
Zisheng FAN
1
;
Yinghui ZHANG
1
;
Xiaomin LUO
1
;
Kaixian CHEN
1
;
Sulin ZHANG
2
;
Hualiang JIANG
3
;
Mingyue ZHENG
4
Author Information
1. Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
2. Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China. slzhang@simm.ac.cn.
3. Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China. hljiang@simm.ac.cn.
4. Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China. myzheng@simm.ac.cn.
- Publication Type:Research Support, Non-U.S. Gov't
- Keywords:
deep learning;
drug target inference;
experimental verification;
transcriptomics
- MeSH:
Drug Delivery Systems;
Proteins;
Transcriptome
- From:
Protein & Cell
2022;13(4):281-301
- CountryChina
- Language:English
-
Abstract:
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 off-target 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.
