ArticleCancer cell2020
Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells.
Article in Cancer cell, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 263 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
263 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Protein Spatial Structure Meets Artificial Intelligence: Revolutionizing Drug Synergy-Antagonism in Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Pooled it
- Visible neural networks for multi-omics integration: a critical review.Frontiers in artificial intelligence · 2025Pooled it
- Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study.BMC cancer · 2024Pooled it
- A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data.BMC bioinformatics · 2023Pooled it
- Biologically-informed integration of drug representations for breast cancer treatment using deep learning.Nature communications · 2025Trial
- Mechanism-Informed Language Modeling and Oxygenated 3D Screening Identify Berberine-Enzalutamide Synergy in Prostate Cancer Models.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- DiM2-DRP: Dual-Input Multi-View Dynamic Contrastive Learning Framework for Drug Response Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Leveraging neural network models for drug repurposing: a case study on cardiac hypertrophy.Scientific reports · 2026Article
- Multi-omics-driven precision medicine.iMeta · 2026Review
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- Article
- Human genetics across levels of biological organization.Nature reviews. Genetics · 2026Review
- DR.DEGMON: self-explainable deep neural network for drug-induced cell viability prediction incorporating differentially expressed genes and gene ontology.BMC medical genomics · 2026Article
- RLASON-CDR: a reinforcement learning-driven adaptive synergistic optimization network for cancer drug response prediction.Briefings in bioinformatics · 2026Article
- seqLens: Optimizing Language Models for Genomic Predictions.Molecular biology and evolution · 2026Article
- Preclinical and Virtual Models of Mucosal Melanoma: Bridging Translational Gaps in a Rare and Lethal Cancer.Pigment cell & melanoma research · 2026Review
- Metronomic Chemotherapy in Ovarian Cancer: Current Scenario and AI-Integrated Future Strategies.Cancer reports (Hoboken, N.J.) · 2026Review
- TF-GateNet: An Interpretable and Biologically Guided Framework for Primary-Metastatic State Prediction from Somatic Genomic Alterations.Biomolecules · 2026Article
- Generation of a stable mouse endometrial tumor-derived cell line using conditional reprogramming.Biological research · 2026Article
- Predicting gene essentiality and drug response from preclinical perturbation screens with layered ensemble of autoencoders and predictors.Scientific reports · 2026Article
203 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Most drugs entering clinical trials fail, often related to an incomplete understanding of the mechanisms governing drug response. Machine learning techniques hold immense promise for better drug response predictions, but most have not reached clinical practice due to their lack of interpretability and their focus on monotherapies. We address these challenges by developing DrugCell, an interpretable deep learning model of human cancer cells trained on the responses of 1,235 tumor cell lines to 684 drugs. Tumor genotypes induce states in cellular subsystems that are integrated with drug structure to predict response to therapy and, simultaneously, learn biological mechanisms underlying the drug response. DrugCell predictions are accurate in cell lines and also stratify clinical outcomes. Analysis of DrugCell mechanisms leads directly to the design of synergistic drug combinations, which we validate systematically by combinatorial CRISPR, drug-drug screening in vitro, and patient-derived xenografts. DrugCell provides a blueprint for constructing interpretable models for predictive medicine.
Indexed as
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What Socratic holds
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.