ArticleNature communications2026
Critical evaluation of drug response prediction models with DrEval.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed.
- Article
- Timesynth: A Temporal Fidelity Framework for Health Signal Digital Twins.Research square · 2026Article
- Tissue-aware elastic net decomposition reveals shared and lineage-specific drug response biomarkers.bioRxiv : the preprint server for biology · 2026Article
- Critical evaluation of drug response prediction models with DrEval.Nature communications · 2026Article
- Mapping drug mechanisms with ProteomicsDB: unified omics and cell sensitivity data at scale.Nucleic acids research · 2026Article
- Integrating artificial intelligence across the cancer drug discovery pipeline using a design-test-refine workflow.Frontiers in oncology · 2026Review
- Leveraging uncertainty estimates for drug response prediction in cancer cell lines.Bioinformatics advances · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Large-scale drug sensitivity screens have enabled training drug response prediction models based on cancer cell line omics profiles to advance personalized medicine. While model performances reported in the literature appear promising, successful translation to the clinic remains limited. In this work, we discuss key obstacles that lead to overly optimistic performance estimates of state-of-the-art models, making it challenging to track progress in the field. To address them, we present DrEval, a pipeline for unbiased, biologically meaningful evaluation of cancer drug response models. DrEval is designed as a living open-source benchmark that integrates baseline and literature models with standardized hyperparameter tuning, statistically rigorous evaluation, cross-study benchmarks, and supports ablation studies and publication-ready visualizations. Using DrEval, we show that deep learning models barely outperform a naive model that predicts only the mean drug and cell line effects, while no complex model outperforms properly tuned tree-based ensemble baselines in relevant settings.
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What OpenQuestion 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 OpenQuestion graph.