ArticleACS omega2018
Prediction of Compound Profiling Matrices Using Machine Learning.
Article in ACS omega, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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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
15 citing papers in PubMed.
- Understanding Conformation Importance in Data-Driven Property Prediction Models.Journal of chemical information and modeling · 2025Article
- PerseuCPP: a machine learning strategy to predict cell-penetrating peptides and their uptake efficiency.Bioinformatics advances · 2025Article
- Identifying Substructures That Facilitate Compounds to Penetrate the Blood-Brain Barrier via Passive Transport Using Machine Learning Explainer Models.ACS chemical neuroscience · 2024Article
- Cell-Penetrating Milk-Derived Peptides with a Non-Inflammatory Profile.Molecules (Basel, Switzerland) · 2023Article
- Biomedical data analyses facilitated by open cheminformatics workflows.Journal of cheminformatics · 2023Article
- Predicting cell-penetrating peptides using machine learning algorithms and navigating in their chemical space.Scientific reports · 2021Article
- Evaluation of multi-target deep neural network models for compound potency prediction under increasingly challenging test conditions.Journal of computer-aided molecular design · 2021Article
- Comparing predictive ability of QSAR/QSPR models using 2D and 3D molecular representations.Journal of computer-aided molecular design · 2021Article
- Prediction of pharmacological activities from chemical structures with graph convolutional neural networks.Scientific reports · 2021Article
- Construction of a web-based nanomaterial database by big data curation and modeling friendly nanostructure annotations.Nature communications · 2020Article
- Cardioprotective Melatonin: Translating from Proof-of-Concept Studies to Therapeutic Use.International journal of molecular sciences · 2019Review
- Unbiased Phenotype Detection Using Negative Controls.SLAS discovery : advancing life sciences R & D · 2019Article
- Article
- Computationally derived compound profiling matrices.Future science OA · 2018Article
- Extracting Compound Profiling Matrices from Screening Data.ACS omega · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Screening of compound libraries against panels of targets yields profiling matrices. Such matrices typically contain structurally diverse screening compounds, large numbers of inactives, and small numbers of hits per assay. As such, they represent interesting and challenging test cases for computational screening and activity predictions. In this work, modeling of large compound profiling matrices was attempted that were extracted from publicly available screening data. Different machine learning methods including deep learning were compared and different prediction strategies explored. Prediction accuracy varied for assays with different numbers of active compounds, and alternative machine learning approaches often produced comparable results. Deep learning did not further increase the prediction accuracy of standard methods such as random forests or support vector machines. Target-based random forest models were prioritized and yielded successful predictions of active compounds for many assays.
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.