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

Feisheng Zhong1,2, Xiaolong Wu1,3, Ruirui Yang1,2,4

  • 1Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.

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|October 22, 2021
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Summary
This summary is machine-generated.

This study introduces a new AI model, the Siamese spectral-based graph convolutional network (SSGCN), to identify chemical compound protein targets using gene expression data. The SSGCN model demonstrates improved accuracy in predicting compound-target interactions compared to existing methods.

Keywords:
deep learningdrug target inferenceexperimental verificationtranscriptomics

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Area of Science:

  • Biomedicine
  • Computational Biology
  • Genomics

Background:

  • Characterizing chemical compounds in cellular contexts is crucial for identifying on-target and off-target effects.
  • Gene transcriptional profiling data offers a new avenue for exploring compound-protein targets via transcriptomics and RNA biology.

Purpose of the Study:

  • To propose and validate a novel Siamese spectral-based graph convolutional network (SSGCN) model for inferring protein targets of chemical compounds from gene expression profiles.
  • To assess the SSGCN model's performance against existing methods like Connectivity Map.

Main Methods:

  • Development of a Siamese spectral-based graph convolutional network (SSGCN) model.
  • Training the SSGCN model on known compound-target pairs to learn correlations between compound perturbation and gene knockdown profiles.
  • Evaluating the model on benchmark and time-split validation datasets.

Main Results:

  • The SSGCN model achieved higher target inference accuracy compared to previous methods.
  • The model successfully learned hidden correlations from gene transcriptional and knockdown profiles.
  • Experimental validation confirmed the practical utility of SSGCN for target inference and inhibitor discovery.

Conclusions:

  • The SSGCN model provides a powerful tool for inferring chemical compound protein targets from gene expression data.
  • SSGCN enhances the understanding of compound mechanisms and aids in drug discovery by identifying novel inhibitors.