ArticleScientific reports2018
Predicting inhibitory and activatory drug targets by chemically and genetically perturbed transcriptome signatures.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Integration of diverse bioactivity data into the Chemical Checker compound universe.Nature protocols · 2025Review
- DTIAM: a unified framework for predicting drug-target interactions, binding affinities and drug mechanisms.Nature communications · 2025Article
- Genetic evidence informs the direction of therapeutic modulation in drug development.npj drug discovery · 2025Article
- New insights into the pharmacological inhibition of SRF activity: Key inhibitory targets and mechanisms.Vascular pharmacology · 2024Review
- Predicting therapeutic and side effects from drug binding affinities to human proteome structures.iScience · 2024Article
- OLB-AC: toward optimizing ligand bioactivities through deep graph learning and activity cliffs.Bioinformatics (Oxford, England) · 2024Article
- Drug target prediction through deep learning functional representation of gene signatures.Nature communications · 2024Article
- Chemo-proteomics in antimalarial target identification and engagement.Medicinal research reviews · 2023Review
- Mechanism of action deconvolution of the small-molecule pathological tau aggregation inhibitor Anle138b.Alzheimer's research & therapy · 2023Article
- Cromolyn prevents cerebral vasospasm and dementia by targeting WDR43.Frontiers in aging neuroscience · 2023Article
- Article
- From drug repositioning to target repositioning: prediction of therapeutic targets using genetically perturbed transcriptomic signatures.Bioinformatics (Oxford, England) · 2022Article
- Prediction of Drug Targets for Specific Diseases Leveraging Gene Perturbation Data: A Machine Learning Approach.Pharmaceutics · 2022Article
- Integration of Transcriptomics Data and Metabolomic Data Using Biomedical Literature Mining and Pathway Analysis.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Comprehensive Survey of Recent Drug Discovery Using Deep Learning.International journal of molecular sciences · 2021Review
- GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data.Journal of cheminformatics · 2021Article
- The novel driver gene ASAP2 is a potential druggable target in pancreatic cancer.Cancer science · 2021Article
- Predicting changes in renal metabolism after compound exposure with a genome-scale metabolic model.Toxicology and applied pharmacology · 2021Article
- Machine Learning Uses Chemo-Transcriptomic Profiles to Stratify Antimalarial Compounds With Similar Mode of Action.Frontiers in cellular and infection microbiology · 2021Article
- An omics perspective on drug target discovery platforms.Briefings in bioinformatics · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genome-wide identification of all target proteins of drug candidate compounds is a challenging issue in drug discovery. Moreover, emerging phenotypic effects, including therapeutic and adverse effects, are heavily dependent on the inhibition or activation of target proteins. Here we propose a novel computational method for predicting inhibitory and activatory targets of drug candidate compounds. Specifically, we integrated chemically-induced and genetically-perturbed gene expression profiles in human cell lines, which avoided dependence on chemical structures of compounds or proteins. Predictive models for individual target proteins were simultaneously constructed by the joint learning algorithm based on transcriptomic changes in global patterns of gene expression profiles following chemical treatments, and following knock-down and over-expression of proteins. This method discriminates between inhibitory and activatory targets and enables accurate identification of therapeutic effects. Herein, we comprehensively predicted drug-target-disease association networks for 1,124 drugs, 829 target proteins, and 365 human diseases, and validated some of these predictions in vitro. The proposed method is expected to facilitate identification of new drug indications and potential adverse effects.
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