ReviewCancer biology & medicine2025
Target identification of natural products in cancer with chemical proteomics and artificial intelligence approaches.
Review in Cancer biology & medicine, 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
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
3 citing papers in PubMed.
- Arctigenin Suppresses Breast Cancer Growth In Vitro and In Vivo Through Subtype-Specific Multi-Targeting Activity.International journal of molecular sciences · 2026Article
- Host Determinants of Immune Checkpoint Inhibitor Efficacy: Immune, Genetic, Metabolic, and Lifestyle Factors.International journal of molecular sciences · 2026Review
- Profiling the Complexity of Resistance Factors in Cancer Cells Towards Berberine and Its Derivatives.Pharmaceuticals (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Natural products (NPs) have long been recognized for their therapeutic potential, especially in cancer treatment, due to an ability to interact with multiple cellular pathways. The identification of molecular targets for NPs is a critical step in understanding anticancer mechanisms, with chemical proteomics emerging as a powerful approach. Both label-based and -free proteomic techniques have been utilized to identify these targets, each with their own advantages and limitations. While label-based methods provide high specificity through chemical tagging, the requirement for labeling can be a limitation, potentially altering NP natural properties. Conversely, label-free techniques allow for the detection of NP-protein interactions without structural modification but may struggle with transient interactions or low-abundance targets. Recent advances in artificial intelligence (AI) have further enhanced the field by improving target prediction and streamlining data analysis. AI-driven models, especially machine learning algorithms, have proven effective in processing complex proteomic data and predicting potential NP-protein interactions. The integration of AI with chemical proteomics accelerates target identification and deepens our understanding of the molecular mechanisms underlying the anticancer effects of NPs. This review explores the application of chemical proteomics and AI in the identification of cancer-related targets for NPs, highlighting current challenges and future directions for clinical translation.
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Identifiers
What Socratic 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 Socratic graph.