ArticlePloS one2024
Unveiling the potential of phytochemicals to inhibit nuclear receptor binding SET domain protein 2 for cancer: Pharmacophore screening, molecular docking, ADME properties, and molecular dynamics simulation investigations.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Targeting hORAI1-Mediated Calcium Influx in Triple-Negative Breast Cancer: A Computational Drug Discovery Approach.The Journal of membrane biology · 2026Article
- In Silico Identification of Novel Compounds as Anthelmintics AgainstAnimals : an open access journal from MDPI · 2025Article
- High-throughput screening of natural antiviral drug candidates against white spot syndrome virus targeting VP28 in Penaeus monodon: Computational drug design approaches.Journal, genetic engineering & biotechnology · 2025Article
- Synthesis, Anticancer Screening, and In Silico Evaluations of Thieno[2,3-Pharmaceuticals (Basel, Switzerland) · 2025Article
- Article
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
Nuclear receptor binding SET domain protein 2 (NSD2) significantly contributes to the development of cancer, making it a promising target for cancer drug discovery. This research explores natural compounds as potential selective inhibitors for NSD2 in cancer treatment. Employing a comprehensive in silico approach, the study utilized pharmacophore modeling, molecular docking, pharmacokinetic profiling, and molecular dynamics simulations. An e-pharmacophore model-based screening using the first selective and potent ligand bound to NSD2 identified 49,248 natural compounds from the SuperNatural 3.0 database (containing 449,008 molecules) with acceptable alignment with the developed pharmacophore hypotheses. Subsequently, molecular docking was executed to assess the standout compounds which led to the selection of ten candidates that surpassed the reference inhibitor in accordance w the binding affinity expressed as a G score. Ligand-residue interaction analyses of the top three hits (SN0450102, SN0410255, and SN0142336) revealed diverse crucial interactions with the NSD2 active site, including hydrogen bonds, pi-pi stacking, and hydrophobic contacts with key amino acid residues in the NSD2-PWWP1 domain. Pharmacokinetic profiling confirmed the drug-likability for the refined hits, indicating good cellular permeability and minimal blood-brain barrier penetration. Molecular dynamics simulations for 200 nanoseconds affirmed the stability of protein-ligand complexes, with minimal fluctuations in root mean square deviation and root mean square fluctuation analyses. Overall, this study identified promising natural compounds as potential pharmaceutical agents in the treatment of NSD2-associated cancers.
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.