ArticleIn silico pharmacology2023
Target validation and structure-based virtual screening to Discover potential lead molecules against the oncogenic NSD1 histone methyltransferase.
Article in In silico pharmacology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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.
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Who cites it
2 citing papers in PubMed, 2 citations in OpenAlex.
- Quantum-Augmented Federated AI for Adaptive Pharmacogenomic Precision Oncology: A Perspective.ACS pharmacology & translational science · 2026Review
- Integrating AI, Machine Learning, and Animal Models for Precision Oncology: Bridging Preclinical and Clinical Gaps.ACS pharmacology & translational science · 2025Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of the study was to validate Nuclear receptor-binding SET Domain NSD1 as a cancer drug target followed by the design of lead molecules against NSD1. TCGA clinical data, molecular expression techniques were used to validate the target and structure-based virtual screening was performed to design hits against NSD1. Clinical data analysis suggests the role of NSD1 in metastasis, prognosis and influence on overall survival in various malignancies. Furthermore, the mRNA and protein expression profile of NSD1 was evaluated in various cell lines. NSD1 was exploited as a target protein for in silico design of inhibitors using two major databases including ZINC15 and ChemDiv by structure-based virtual screening approach. Virtual screening was performed using the pharmacophore hypothesis designed with a protein complex S-adenosyl-l-methionine (SAM) as an endogenous ligand. Subsequently, a combined score was used to distinguish the top 10 compounds from the docking screened compounds having high performance in all four scores (docking score, XP, Gscore, PhaseScreenScore, and MMGBSA delta G Bind). Finally, the top three Zinc compounds were subjected to molecular dynamic simulation. The binding MMGBSA data suggests that ZINC000257261703 and ZINC000012405780 can be taken for in vitro and in vivo studies as they have lesser MMGBSA energy towards the cofactor binding site of NSD1 than the sinefungin. Our data validates NSD1 as a cancer drug target and provides promising structures that can be utilized for further lead optimization and rational drug design to open new gateways in the field of cancer therapeutics. Graphical abstract: Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-023-00158-0.
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