ArticleiScience2020
Non-linear Deep Neural Network for Rapid and Accurate Prediction of Phenotypic Responses to Kinase Inhibitors.
Article in iScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 15 citations in OpenAlex.
- Enhancing accuracy of virtual kinase profiling via application of graph neural network to 3D pharmacophore ensembles.Journal of computer-aided molecular design · 2025Article
- Inference of drug off-target effects on cellular signaling using interactome-based deep learning.iScience · 2024Article
- Polypharmacology-based kinome screen identifies new regulators of KSHV reactivation.PLoS pathogens · 2023Article
- AiKPro: deep learning model for kinome-wide bioactivity profiling using structure-based sequence alignments and molecular 3D conformer ensemble descriptors.Scientific reports · 2023Article
- Polypharmacology-based kinome screen identifies new regulators of KSHV reactivation.bioRxiv : the preprint server for biology · 2023Article
- Deep neural network modeling identifies biomarkers of response to immune-checkpoint therapy.iScience · 2022Article
- Drug Repositioning and Subgroup Discovery for Precision Medicine Implementation in Triple Negative Breast Cancer.Cancers · 2021Article
- Utilizing preclinical models to develop targeted therapies for rare central nervous system cancers.Neuro-oncology · 2021Review
- Machine learning identifies molecular regulators and therapeutics for targeting SARS-CoV2-induced cytokine release.Molecular systems biology · 2021Article
- Nanomedicine of tyrosine kinase inhibitors.Theranostics · 2021Review
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 at 1 institution in 1 country.
Funding
Abstract
Protein kinase inhibitors are one of the most successful targeted therapies to date. Despite this progress, additional kinase inhibitors are needed to expand the target space as well as overcome drug resistance that has emerged in clinical setting. Here, we developed KiDNN (Kinase inhibitor prediction using Deep Neural Networks). KiDNN utilizes non-linear, multilayer feedforward network that mimics complex and dynamic kinase-driven signaling pathways. We used KiDNN to predict the effect of ∼200 kinase inhibitors on migration of breast and liver cancer cells. We show that the prediction accuracy of KiDNN outperformed other prediction tools based on linear models. We validated that an inhibitor of tyrosine kinase receptors, and an inhibitor of Src family kinases, decreased migration of triple-negative breast cancer cells, consistent with the role of these kinases in driving motility. Overall, we show that non-linear, DNN-based models provide a powerful approach to in silico screen hundreds of kinase inhibitors.
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.