ArticleNature communications2021
Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 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.
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Who cites it
59 citing papers in PubMed, 125 citations in OpenAlex.
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- Machine learning for drug-target interaction prediction: A comprehensive review of models, challenges, and computational strategies.Computational and structural biotechnology journal · 2026Review
- TRANSPIRE-DRP: a deep learning framework for translating patient-derived xenograft drug response to clinical patients via domain adaptation.Journal of translational medicine · 2025Article
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Progress of AI-Driven Drug-Target Interaction Prediction and Lead Optimization.International journal of molecular sciences · 2025Review
- Physicians' Attitudes Toward Artificial Intelligence in Medicine: Mixed Methods Survey and Interview Study.Journal of medical Internet research · 2025Article
- Understanding the sources of performance in deep drug response models reveals insights and improvements.Bioinformatics (Oxford, England) · 2025Article
- Therapeutic target prediction for orphan diseases integrating genome-wide and transcriptome-wide association studies.Nature communications · 2025Article
- New horizons at the interface of artificial intelligence and translational cancer research.Cancer cell · 2025Review
- High-Throughput Empirical and Virtual Screening To Discover Novel Inhibitors of Polyploid Giant Cancer Cells in Breast Cancer.Analytical chemistry · 2025Article
- Drug response in the era of precision medicine: A methodological review.Computational and structural biotechnology journal · 2025Review
- Predictive modelling and ranking:Frontiers in chemistry · 2025Article
- Machine learning-aided discovery of T790M-mutant EGFR inhibitor CDDO-Me effectively suppresses non-small cell lung cancer growth.Cell communication and signaling : CCS · 2024Article
- Article
- High-Throughput Empirical and Virtual Screening to Discover Novel Inhibitors of Polyploid Giant Cancer Cells in Breast Cancer.bioRxiv : the preprint server for biology · 2024Article
- Attribute-guided prototype network for few-shot molecular property prediction.Briefings in bioinformatics · 2024Article
- A comprehensive review of computational cell cycle models in guiding cancer treatment strategies.NPJ systems biology and applications · 2024Review
- Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data.Diabetologia · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
Abstract
Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients. Here, we present an approach, named Drug Ranking Using ML (DRUML), which uses omics data to produce ordered lists of >400 drugs based on their anti-proliferative efficacy in cancer cells. To reduce noise and increase predictive robustness, instead of individual features, DRUML uses internally normalized distance metrics of drug response as features for ML model generation. DRUML is trained using in-house proteomics and phosphoproteomics data derived from 48 cell lines, and it is verified with data comprised of 53 cellular models from 12 independent laboratories. We show that DRUML predicts drug responses in independent verification datasets with low error (mean squared error < 0.1 and mean Spearman's rank 0.7). In addition, we demonstrate that DRUML predictions of cytarabine sensitivity in clinical leukemia samples are prognostic of patient survival (Log rank p < 0.005). Our results indicate that DRUML accurately ranks anti-cancer drugs by their efficacy across a wide range of pathologies.
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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.