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Related Experiment Video

Updated: Apr 11, 2026

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English
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Predictive bioactivity modeling and structural binding analysis for the identification of potential SMYD3 modulators.

Abdullah R Alzahrani1, Zia Ur Rehman2,3, Talha Jawaid4

  • 1Department of Pharmacology and Toxicology, Faculty of Medicine, Umm Al-Qura University, P.O. Box 13578, Al-Abidiyah, Makkah, 21955, Saudi Arabia.

Molecular Diversity
|April 10, 2026
PubMed
Summary

This study developed a computational framework to identify SMYD3 inhibitors, a key target in cancer. CHEMBL4472528 emerged as a promising lead compound for further drug development.

Keywords:
SMYD3CancerMachine learning QSARMolecular modellingNetwork biologyPrediction model

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Area of Science:

  • Biochemistry and Molecular Biology
  • Computational Chemistry
  • Pharmacology

Background:

  • SMYD3 (SET and MYND domain-containing protein 3) is a lysine methyltransferase implicated in epigenetic regulation and oncogenic transcription.
  • Its role in cancer makes SMYD3 a significant therapeutic target, yet challenges exist in developing effective inhibitors.

Purpose of the Study:

  • To establish an integrated computational workflow for prioritizing potential SMYD3 inhibitors.
  • To identify novel chemical scaffolds with therapeutic potential against SMYD3.

Main Methods:

  • Machine learning-based quantitative structure-activity relationship (QSAR) modeling using diverse descriptors and algorithms.
  • External bioactivity prediction, molecular docking, and molecular dynamics (MD) simulations.
  • Network and pathway analysis to contextualize SMYD3's biological role.

Main Results:

  • A Random Forest model based on MACCS fingerprints demonstrated robust predictive performance.
  • SHAP analysis identified key structural features influencing SMYD3 activity.
  • Screening identified CHEMBL4472528 as a stable SMYD3 inhibitor with favorable binding characteristics.
  • Network analysis confirmed SMYD3's involvement in chromatin regulation and transcriptional processes.

Conclusions:

  • The developed computational framework is reproducible and effective for SMYD3 inhibitor discovery.
  • CHEMBL4472528 represents a promising scaffold for developing targeted SMYD3-based cancer therapies.