ArticleNature communications2024
Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- DRIVE: a comprehensive resource deciphering drug-induced transcriptomic and splicing response in cancer cell.Neoplasia (New York, N.Y.) · 2026Article
- DECANT: decoupling mechanism from context in single-cell drug perturbation representation.Bioinformatics (Oxford, England) · 2026Article
- scBalFlow: a staged flow matching framework for imbalanced single-cell drug perturbation prediction.Bioinformatics (Oxford, England) · 2026Article
- Enhancing cross-context generalization in drug perturbation prediction with a multimodal conditional diffusion framework.Bioinformatics (Oxford, England) · 2026Article
- Model-based inference of enzyme inhibitions from perturbation-induced metabolic dynamics.bioRxiv : the preprint server for biology · 2026Article
- Advances in high-throughput drug screening based on pharmacotranscriptomics.Journal of advanced research · 2026Review
- Benchmarking the prediction of responding cells to perturbations affecting both gene expression and cellular abundance using scRNA sequencing.Scientific reports · 2026Article
- ExPO: an exposure-conditioned neural operator for L1000 signature prediction.Journal of cheminformatics · 2026Article
- DeepICER: A deep learning framework for predicting compound-induced gene expression profiles.Acta pharmaceutica Sinica. B · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Article
- Predicting condition-aware drug-induced transcriptional responses via a latent diffusion model.Bioinformatics (Oxford, England) · 2026Article
- A review of recent advances in generative artificial intelligence models for biomolecular sciences.Acta pharmaceutica Sinica. B · 2026Review
- Predicting drug-perturbed transcriptional responses using multi-conditional diffusion transformer.Quantitative biology (Beijing, China) · 2026Article
- Benchmarking algorithms for generalizable single-cell perturbation response prediction.Nature methods · 2026Article
- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- CCCdb: a comprehensive manually curated database for cell-cell communication in human and mouse.Nucleic acids research · 2026Article
- Therapeutic target database 2026: facilitating targeted therapies and precision medicine.Nucleic acids research · 2026Article
- Integrating artificial intelligence across the cancer drug discovery pipeline using a design-test-refine workflow.Frontiers in oncology · 2026Review
- Squidiff: predicting cellular development and responses to perturbations using a diffusion model.Nature methods · 2026Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding transcriptional responses to chemical perturbations is central to drug discovery, but exhaustive experimental screening of disease-compound combinations is unfeasible. To overcome this limitation, here we introduce PRnet, a perturbation-conditioned deep generative model that predicts transcriptional responses to novel chemical perturbations that have never experimentally perturbed at bulk and single-cell levels. Evaluations indicate that PRnet outperforms alternative methods in predicting responses across novel compounds, pathways, and cell lines. PRnet enables gene-level response interpretation and in-silico drug screening for diseases based on gene signatures. PRnet further identifies and experimentally validates novel compound candidates against small cell lung cancer and colorectal cancer. Lastly, PRnet generates a large-scale integration atlas of perturbation profiles, covering 88 cell lines, 52 tissues, and various compound libraries. PRnet provides a robust and scalable candidate recommendation workflow and successfully recommends drug candidates for 233 diseases. Overall, PRnet is an effective and valuable tool for gene-based therapeutics screening.
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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.