ArticleSLAS technology2021
Artificial Intelligence (AI) to the Rescue: Deploying Machine Learning to Bridge the Biorelevance Gap in Antioxidant Assays.
Article in SLAS technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 35 citations in OpenAlex.
- Oxidative stress in cancer: from tumor and microenvironment remodeling to therapeutic frontiers.Molecular cancer · 2025Review
- Oxidative Stress and Mitochondrial Dysfunction in Myelodysplastic Syndrome: Roles in Development, Diagnosis, Prognosis, and Treatment.International journal of molecular sciences · 2025Review
- Smart estimation of protective antioxidant enzymes' activity in savory (Satureja rechingeri L.) under drought stress and soil amendments.BMC plant biology · 2025Article
- Therapeutic effect of targeted antioxidant natural products.Discover nano · 2024Review
- Machine learning approaches to detect hepatocyte chromatin alterations from iron oxide nanoparticle exposure.Scientific reports · 2024Article
- Phytochemicals in Drug Discovery-A Confluence of Tradition and Innovation.International journal of molecular sciences · 2024Review
- Probabilistic Modelling of the Food Matrix Effects on Curcuminoid's In Vitro Oral Bioaccessibility.Foods (Basel, Switzerland) · 2024Article
- Bioactivity and Bioavailability of Carotenoids Applied in Human Health: Technological Advances and Innovation.International journal of molecular sciences · 2024Review
- Employing Supervised Algorithms for the Prediction of Nanomaterial's Antioxidant Efficiency.International journal of molecular sciences · 2023Review
- PARP-1-Associated Pathological Processes: Inhibition by Natural Polyphenols.International journal of molecular sciences · 2021Review
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Oxidative stress induced by excessive levels of reactive oxygen species (ROS) underlies several diseases. Therapeutic strategies to combat oxidative damage are, therefore, a subject of intense scientific investigation to prevent and treat such diseases, with the use of phytochemical antioxidants, especially polyphenols, being a major part. Polyphenols, however, exhibit structural diversity that determines different mechanisms of antioxidant action, such as hydrogen atom transfer (HAT) and single-electron transfer (SET). They also suffer from inadequate in vivo bioavailability, with their antioxidant bioactivity governed by permeability, gut-wall and first-pass metabolism, and HAT-based ROS trapping. Unfortunately, no current antioxidant assay captures these multiple dimensions to be sufficiently "biorelevant," because the assays tend to be unidimensional, whereas biorelevance requires integration of several inputs. Finding a method to reliably evaluate the antioxidant capacity of these phytochemicals, therefore, remains an unmet need. To address this deficiency, we propose using artificial intelligence (AI)-based machine learning (ML) to relate a polyphenol's antioxidant action as the output variable to molecular descriptors (factors governing in vivo antioxidant activity) as input variables, in the context of a biomarker selectively produced by lipid peroxidation (a consequence of oxidative stress), for example F
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