ArticleJournal of cheminformatics2019
KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images.
Article in Journal of cheminformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- Innovative strategies to overcome cancer drug resistance: from nanodelivery systems to targeted therapeutics.Discover oncology · 2026Review
- TransCell: In Silico Characterization of Genomic Landscape and Cellular Responses by Deep Transfer Learning.Genomics, proteomics & bioinformatics · 2024Article
- Image-based molecular representation learning for drug development: a survey.Briefings in bioinformatics · 2024Review
- KLSD: a kinase database focused on ligand similarity and diversity.Frontiers in pharmacology · 2024Article
- Machine Learning-Enabled Genome Mining and Bioactivity Prediction of Natural Products.ACS synthetic biology · 2023Review
- MOViDA: multiomics visible drug activity prediction with a biologically informed neural network model.Bioinformatics (Oxford, England) · 2023Article
- Evaluating molecular representations in machine learning models for drug response prediction and interpretability.Journal of integrative bioinformatics · 2022Article
- CancerOmicsNet: a multi-omics network-based approach to anti-cancer drug profiling.Oncotarget · 2022Article
- Prediction of activity cliffs on the basis of images using convolutional neural networks.Journal of computer-aided molecular design · 2021Article
- Performance Comparisons of AlexNet and GoogLeNet in Cell Growth Inhibition IC50 Prediction.International journal of molecular sciences · 2021Article
- Learning curves for drug response prediction in cancer cell lines.BMC bioinformatics · 2021Article
- Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data.Drug discovery today · 2021Review
- Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts.Journal of chemical information and modeling · 2021Article
- Prediction of pharmacological activities from chemical structures with graph convolutional neural networks.Scientific reports · 2021Article
- QSAR-derived affinity fingerprints (part 2): modeling performance for potency prediction.Journal of cheminformatics · 2020Article
- Activity landscape image analysis using convolutional neural networks.Journal of cheminformatics · 2020Article
- An Analysis of Proteochemometric and Conformal Prediction Machine Learning Protein-Ligand Binding Affinity Models.Frontiers in molecular biosciences · 2020Article
- Reply to "Missed opportunities in large scale comparison of QSAR and conformal prediction methods and their applications in drug discovery".Journal of cheminformatics · 2019Article
- DeepSnap-Deep Learning Approach Predicts Progesterone Receptor Antagonist Activity With High Performance.Frontiers in bioengineering and biotechnology · 2019Article
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
Abstract
The application of convolutional neural networks (ConvNets) to harness high-content screening images or 2D compound representations is gaining increasing attention in drug discovery. However, existing applications often require large data sets for training, or sophisticated pretraining schemes. Here, we show using 33 IC
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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.