ArticleNature machine intelligence2022
A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening.
Article in Nature machine intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
42 citing papers in PubMed.
- TMEDRP: decoding tumor-intrinsic and microenvironmental signatures for clinical drug response prediction.Bioinformatics (Oxford, England) · 2026Article
- A disentangled transformer-based transfer learning framework to predict patient drug response from tumor single-cell transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- Predicting gene essentiality and drug response from preclinical perturbation screens with layered ensemble of autoencoders and predictors.Scientific reports · 2026Article
- FourierDrug: a domain generalization framework for robust drug response prediction via frequency-space asymmetric attention.Bioinformatics (Oxford, England) · 2026Article
- Deep learning for predicting patient drug response by transferring gene-level and cell-level knowledge to tumors.NPJ precision oncology · 2026Article
- MDCDR: predicting cancer drug response via multimodal feature fusion and feature disentanglement.Briefings in bioinformatics · 2026Article
- The MOGA multi-modal framework based on graph augmentation networks for drug response prediction.iScience · 2026Article
- Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysis.Briefings in bioinformatics · 2026Article
- A Knowledge-Enhanced Multimodal Framework with Genomic Reconstruction for DLBCL Drug Response Prediction.Computational and structural biotechnology journal · 2026Article
- Challenges and opportunities for oncology drug repurposing informed by synthetic lethality.NPJ systems biology and applications · 2025Article
- AI-driven virtual cell models in preclinical research: technical pathways, validation mechanisms, and clinical translation potential.NPJ digital medicine · 2025Review
- TRANSPIRE-DRP: a deep learning framework for translating patient-derived xenograft drug response to clinical patients via domain adaptation.Journal of translational medicine · 2025Article
- AI-powered programmable virtual humans toward human physiologically-based drug discovery.Drug discovery today · 2025Review
- Integrating artificial intelligence into small molecule development for precision cancer immunomodulation therapy.npj drug discovery · 2025Review
- Fast-adapting graph neural network with prior knowledge for drug response prediction across preclinical and clinical data.Journal of pharmaceutical analysis · 2025Article
- Ophthalmic drug discovery and development using artificial intelligence and digital health technologies.NPJ digital medicine · 2025Review
- DTLCDR: A target-based multimodal fusion deep learning framework for cancer drug response prediction.Journal of pharmaceutical analysis · 2025Article
- Understanding the sources of performance in deep drug response models reveals insights and improvements.Bioinformatics (Oxford, England) · 2025Article
- A disentangled generative model for improved drug response prediction in patients via sample synthesis.Journal of pharmaceutical analysis · 2025Article
- A self-conformation-aware pre-training framework for molecular property prediction with substructure interpretability.Nature communications · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Accurate and robust prediction of patient-specific responses to a new compound is critical to personalized drug discovery and development. However, patient data are often too scarce to train a generalized machine learning model. Although many methods have been developed to utilize cell-line screens for predicting clinical responses, their performances are unreliable owing to data heterogeneity and distribution shift. Here we have developed a novel context-aware deconfounding autoencoder (CODE-AE) that can extract intrinsic biological signals masked by context-specific patterns and confounding factors. Extensive comparative studies demonstrated that CODE-AE effectively alleviated the out-of-distribution problem for the model generalization and significantly improved accuracy and robustness over state-of-the-art methods in predicting patient-specific clinical drug responses purely from cell-line compound screens. Using CODE-AE, we screened 59 drugs for 9,808 patients with cancer. Our results are consistent with existing clinical observations, suggesting the potential of CODE-AE in developing personalized therapies and drug response biomarkers.
Identifiers
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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.