ArticleACS omega2022
Predicting the Redox Potentials of Phenazine Derivatives Using DFT-Assisted Machine Learning.
Article in ACS omega, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 25 citations in OpenAlex.
- High-Energy-Density Redox Flow Batteries: Mechanisms, Design Strategies, and Recent Progress.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Review
- Article
- Investigating Synergistic Strategies: Integrating Linear Regression, Quantum Mechanics, and Molecular Dynamics for the Discovery of Novel Anticancer Drugs Targeting MTH1 Inhibition.Current medicinal chemistry · 2026Article
- Rapid discovery of Transglutaminase 2 inhibitors for celiac disease with boosting ensemble machine learning.Computational and structural biotechnology journal · 2024Article
- Unlocking the Potential: Predicting Redox Behavior of Organic Molecules, from Linear Fits to Neural Networks.Journal of chemical theory and computation · 2023Review
- Machine learning for flow batteries: opportunities and challenges.Chemical science · 2022Review
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study investigates four machine-learning (ML) models to predict the redox potentials of phenazine derivatives in dimethoxyethane using density functional theory (DFT). A small data set of 151 phenazine derivatives having only one type of functional group per molecule (20 unique groups) was used for the training. Prediction accuracy was improved by a combined strategy of feature selection and hyperparameter optimization, using the external validation set. Models were evaluated on the external test set containing new functional groups and diverse molecular structures. High prediction accuracies of
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.