ArticleNature biotechnology2021
Prediction of drug efficacy from transcriptional profiles with deep learning.
Article in Nature biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
88 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation.International journal of molecular sciences · 2023Pooled it
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- Full-Body AI Agent: A Perspective on Multi-Scale Collaborative AI for Systemic Biology and Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Advances in high-throughput drug screening based on pharmacotranscriptomics.Journal of advanced research · 2026Review
- Review
- Combinatorial prediction of therapeutic perturbations using causally inspired neural networks.Nature biomedical engineering · 2026Article
- DeepICER: A deep learning framework for predicting compound-induced gene expression profiles.Acta pharmaceutica Sinica. B · 2026Article
- Advances in Pharmacotherapy and Physiotherapy for Dry Eye Disease: Molecular Mechanisms and Future Directions-A Narrative Literature Review.International journal of molecular sciences · 2026Review
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- Single-cell-based identification of drug synergy with immunotherapy via tumor microenvironment remodeling.Journal for immunotherapy of cancer · 2026Article
- Predicting drug-perturbed transcriptional responses using multi-conditional diffusion transformer.Quantitative biology (Beijing, China) · 2026Article
- Antimycin A inhibits alpha-herpesvirus replication by disrupting the formation of pyrimidinosomes.Journal of advanced research · 2026Article
- ViMDT: a clinical data visual analysis tool for multi-disciplinary treatment of lung cancer.Clinical and experimental medicine · 2026Article
- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- Omics-based large language models: A new engine for drug discovery innovation.Acta pharmaceutica Sinica. B · 2026Review
- 3D Cardiac Constructs in Drug Discovery: Current Advances and Future Challenges.Research (Washington, D.C.) · 2026Review
- Perception of artificial intelligence and machine learning applications in the Nigerian healthcare sector: A cross-sectional study.PLOS global public health · 2026Article
- SynergyGraph: predicting cell line specific drug combination synergy scores using knowledge graph representation and hypergraph modeling.Scientific reports · 2025Article
28 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug discovery focused on target proteins has been a successful strategy, but many diseases and biological processes lack obvious targets to enable such approaches. Here, to overcome this challenge, we describe a deep learning-based efficacy prediction system (DLEPS) that identifies drug candidates using a change in the gene expression profile in the diseased state as input. DLEPS was trained using chemically induced changes in transcriptional profiles from the L1000 project. We found that the changes in transcriptional profiles for previously unexamined molecules were predicted with a Pearson correlation coefficient of 0.74. We examined three disorders and experimentally tested the top drug candidates in mouse disease models. Validation showed that perillen, chikusetsusaponin IV and trametinib confer disease-relevant impacts against obesity, hyperuricemia and nonalcoholic steatohepatitis, respectively. DLEPS can generate insights into pathogenic mechanisms, and we demonstrate that the MEK-ERK signaling pathway is a target for developing agents against nonalcoholic steatohepatitis. Our findings suggest that DLEPS is an effective tool for drug repurposing and discovery.
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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 OpenQuestion graph.