ArticleACS omega2025
Predicting Substrate Reactivity in Oxidative Homocoupling of Phenols Using Positive and Unlabeled Machine Learning.
Article in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Competition-Derived Relative Reactivity and 3D Electronic-State Analysis of Site- and Facial Selectivity in NaBHJACS Au · 2026Article
- Yield Prediction of Organic Reactions in Biased Data Sets via Positive-Unlabeled Learning.Journal of the American Chemical Society · 2026Article
- DPPH Measurement for Phenols and Prediction of Antioxidant Activity of Phenolic Compounds in Food.Current issues in molecular biology · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A positive and unlabeled machine learning (PU learning) model was trained to predict substrate reactivity in the oxidative homocoupling of phenols under different conditions. We demonstrated its effectiveness by conducting validation using two descriptor sets: 28-dimensional descriptors considered to influence reactivity and extended-connectivity fingerprints. We performed parameter tuning of the model using our experimental data and determined that the optimized parameters provided excellent prediction accuracy for the existing experimental data, regardless of the reaction conditions. Furthermore, the prediction results obtained using 30 types of unlabeled data matched the experimental results for approximately 83.3-86.7% of substrates, and the prediction accuracy of the PU learning model was shown to be superior to that of a model trained with both positive and negative reactivity data. Because negative data are not required to train a PU learning model, it can be applied to reactions reported in many previous studies, informing the cost-effective synthesis of molecules based on model-predicted results.
Identifiers
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Registered trials
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