ArticleChemical science2018
Machine intelligence decrypts β-lapachone as an allosteric 5-lipoxygenase inhibitor.
Article in Chemical science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed, 68 citations in OpenAlex.
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- Controlled Self-Immolative Release of β-Lapachone via an OptimizedJournal of the American Chemical Society · 2025Article
- Computer-aided Drug Discovery Approaches in the Identification of Anticancer Drugs from Natural Products: A Review.Current computer-aided drug design · 2025Review
- Machine Learning Uncovers Natural Product Modulators of the 5-Lipoxygenase Pathway and Facilitates the Elucidation of Their Biological Mechanisms.ACS chemical biology · 2024Article
- Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics.Current pharmaceutical design · 2024Review
- The rise of automated curiosity-driven discoveries in chemistry.Chemical science · 2023Review
- Applications of multi-omics analysis in human diseases.MedComm · 2023Review
- Expanding Transition Metal-Mediated Bioorthogonal Decaging to Include C-C Bond Cleavage Reactions.Journal of the American Chemical Society · 2023Article
- Machine learning and protein allostery.Trends in biochemical sciences · 2023Review
- Artificial intelligence in microbial natural product drug discovery: current and emerging role.Natural product reports · 2022Review
- Allostery: Allosteric Cancer Drivers and Innovative Allosteric Drugs.Journal of molecular biology · 2022Review
- Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance.Bioengineering (Basel, Switzerland) · 2022Review
- Controlled masking and targeted release of redox-cycling ortho-quinones via a C-C bond-cleaving 1,6-elimination.Nature chemistry · 2022Article
- Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries.Molecular diversity · 2022Review
- Evaluation guidelines for machine learning tools in the chemical sciences.Nature reviews. Chemistry · 2022Review
- Natural product drug discovery in the artificial intelligence era.Chemical science · 2022Review
- Landomycins as glutathione-depleting agents and natural fluorescent probes for cellular Michael adduct-dependent quinone metabolism.Communications chemistry · 2021Article
- Artificial intelligence to deep learning: machine intelligence approach for drug discovery.Molecular diversity · 2021Review
- Allosteric Antagonist Modulation of TRPV2 by Piperlongumine Impairs Glioblastoma Progression.ACS central science · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 3 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Using machine learning, targets were identified for β-lapachone. Resorting to biochemical assays, β-lapachone was validated as a potent, ligand efficient, allosteric and reversible modulator of 5-lipoxygenase (5-LO). Moreover, we provide a rationale for 5-LO modulation and show that inhibition of 5-LO is relevant for the anticancer activity of β-lapachone. This work demonstrates the power of machine intelligence to deconvolute complex phenotypes, as an alternative and/or complement to chemoproteomics and as a viable general approach for systems pharmacology studies.
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.