ArticleProtein & cell2022
Drug target inference by mining transcriptional data using a novel graph convolutional network framework.
Article in Protein & cell, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Preventive and therapeutic benefits of nelfinavir in rhesus macaques and human beings infected with SARS-CoV-2.Signal transduction and targeted therapy · 2023Trial
- 3D Geometric Equivariant Graph Neural Networks with Contrastive Learning for Protein-Ligand Binding Affinity Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Drug target prediction from perturbation transcriptomics via a biological function-guided hypergraph siamese network.Bioinformatics (Oxford, England) · 2026Article
- Intelligence on the graph: Graph neural networks for mechanistic drug target discovery.Journal of pharmaceutical analysis · 2026Review
- Advances in high-throughput drug screening based on pharmacotranscriptomics.Journal of advanced research · 2026Review
- Investigation of Intercellular Trafficking of Global Glycosylphosphatidylinositol-Anchored Proteins Using Cell Metabolic Engineering and Transwell Coculture.Angewandte Chemie (International ed. in English) · 2026Article
- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- Rethinking network analysis in ethnopharmacology: a multi-omics and AI roadmap to overcome conceptual and methodological biases.Frontiers in pharmacology · 2026Article
- Omics-based large language models: A new engine for drug discovery innovation.Acta pharmaceutica Sinica. B · 2026Review
- Advancing active compound discovery for novel drug targets: insights from AI-driven approaches.Acta pharmacologica Sinica · 2025Review
- SIRT5: a potential target for discovering bioactive natural products.Journal of natural medicines · 2025Review
- Ginkgetin Alleviates Inflammation and Senescence by Targeting STING.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Identifying compound-protein interactions with knowledge graph embedding of perturbation transcriptomics.Cell genomics · 2024Article
- Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery.Nature communications · 2024Article
- Tumor Exosomal ENPP1 Hydrolyzes cGAMP to Inhibit cGAS-STING Signaling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Drug target prediction through deep learning functional representation of gene signatures.Nature communications · 2024Article
- Gut microbiome as therapeutic target for diabesity management: opportunity for nanonutraceuticals and associated challenges.Drug delivery and translational research · 2024Review
- Machine Learning Research Trends in Traditional Chinese Medicine: A Bibliometric Review.International journal of general medicine · 2024Review
- An overview of recent advances and challenges in predicting compound-protein interaction (CPI).Medical review (2021) · 2023Review
- MDTips: a multimodal-data-based drug-target interaction prediction system fusing knowledge, gene expression profile, and structural data.Bioinformatics (Oxford, England) · 2023Article
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.