ArticlePloS one2019
Predicting drug activity against cancer cells by random forest models based on minimal genomic information and chemical properties.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 2 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
42 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Exploring the role of artificial intelligence in chemotherapy development, cancer diagnosis, and treatment: present achievements and future outlook.Frontiers in oncology · 2025Pooled it
- Exploring alternative approaches to precision medicine through genomics and artificial intelligence - a systematic review.Frontiers in medicine · 2023Pooled it
- MAMMAL - Molecular Aligned Multi-Modal Architecture and Language for biomedical discovery.npj drug discovery · 2026Article
- A diagnostic model for discrimination between Pneumocystis jirovecii pneumonia and colonization based on multiple parameters.Scientific reports · 2026Article
- Computational characterization and machine learning analysis of quantum optimized marine fungal metabolites targeting PD-L1 in cancer immunotherapy.Journal of computer-aided molecular design · 2025Article
- TRANSPIRE-DRP: a deep learning framework for translating patient-derived xenograft drug response to clinical patients via domain adaptation.Journal of translational medicine · 2025Article
- Microenvironmental signals combine to induce non-additive molecular and phenotypic responses in mammary epithelial cells.iScience · 2025Article
- From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine.Health information science and systems · 2024Review
- Review
- Identification of Potential Tryptase Inhibitors from FDA-Approved Drugs Using Machine Learning, Molecular Docking, and Experimental Validation.ACS omega · 2024Article
- The Artificial Intelligence-Powered New Era in Pharmaceutical Research and Development: A Review.AAPS PharmSciTech · 2024Review
- Application of peripheral blood routine parameters in the diagnosis of influenza and Mycoplasma pneumoniae.Virology journal · 2024Article
- A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations.Cell reports methods · 2024Article
- Virtual Screening of Small Molecules Targeting BCL2 with Machine Learning, Molecular Docking, and MD Simulation.Biomolecules · 2024Article
- Deconvoluting low yield from weak potency in direct-to-biology workflows with machine learning.RSC medicinal chemistry · 2024Article
- Small molecule autoencoders: architecture engineering to optimize latent space utility and sustainability.Journal of cheminformatics · 2024Article
- A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening.Cancers · 2024Article
- Artificial intelligence in predicting pathogenic microorganisms' antimicrobial resistance: challenges, progress, and prospects.Frontiers in cellular and infection microbiology · 2024Review
- Computational Approaches: A New Frontier in Cancer Research.Combinatorial chemistry & high throughput screening · 2024Review
- Identification and validation of microtubule depolymerizing agent, CYT997, as a potential drug candidate for hepatocellular carcinoma.Liver international : official journal of the International Association for the Study of the Liver · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A key goal of precision medicine is predicting the best drug therapy for a specific patient from genomic information. In oncology, cancers that appear similar pathologically can vary greatly in how they respond to the same drug. Fortunately, data from high-throughput screening programs often reveal important relationships between genomic variability of cancer cells and their response to drugs. Nevertheless, many current computational methods to predict compound activity against cancer cells require large quantities of genomic, epigenomic, and additional cellular data to develop and to apply. Here we integrate recent screening data and machine learning to train classification models that predict the activity/inactivity of compounds against cancer cells based on the mutational status of only 145 oncogenes and a set of compound structural descriptors. Using IC50 values of 1 μM as activity cutoffs, our predictive models have sensitivities of 87%, specificities of 87%, and yield an area under the receiver operating characteristic curve equal to 0.94. We also develop regression models to predict log(IC50) values of compounds for cancer cells; the models achieve a Pearson correlation coefficient of 0.86 for cross-validation and up to 0.65-0.73 against blind test sets. Predictive performance remains strong when as few as 50 oncogenes are included. Finally, even when 40% of experimental IC50 values are missing from screening data, they can be imputed with sufficient reliability that classification accuracy is not diminished. The presented models are fast to generate and may serve as easily implemented screening tools for personalized oncology medicine, drug repurposing, and drug discovery.
Indexed as
Identifiers
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.