ArticleIn silico pharmacology2025
Computational assessment of natural compounds as potential hexokinase 2 inhibitors.
Article in In silico pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hexokinase 2 (HK2), a central enzyme in the glycolytic pathway, is frequently overexpressed in malignant cells, contributing to enhanced glucose metabolism and tumor progression. Despite its recognized role in cancer metabolism, effective and selective HK2 inhibitors from natural sources remain underexplored. This study aimed to identify promising natural product inhibitors of HK2 through an integrated computational approach. A diverse set of 100 natural compounds-including phenolics, terpenoids, alkaloids, and anthraquinones-was virtually screened using Glide XP docking to predict binding affinities. Subsequent molecular dynamics (MD) simulations and MM-GBSA binding free energy calculations were performed to validate docking results and evaluate complex stability over time. Notably, several phenolics, particularly salvianolic acid A, salvianolic acid B, verbascoside, quercetin, and hyperoside, exhibited strong binding affinities and favorable interaction profiles. Salvianolic acid A recorded the best docking score (- 9.58 kcal/mol), while hyperoside showed exceptional structural stability during 30 ns MD simulations and a competitive MM-GBSA binding energy (- 42.35 kcal/mol), approaching that of the native ligand (- 55.51 kcal/mol). Structural interaction analysis revealed that hyperoside formed persistent hydrogen bonds with key catalytic residues of HK2 (Glu742, Asp657, Glu708), supporting its potential as a lead scaffold. These findings highlight the value of phenolic natural products as potential HK2 inhibitors and lay the groundwork for future anticancer drug development targeting cancer-specific metabolic pathways.
Indexed as
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.