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Updated: Sep 3, 2025

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
Published on: May 9, 2025
An alignment-independent three-dimensional quantitative structure-activity relationship study on ron receptor
Omid Zarei1, Stéphane L Raeppel2, Maryam Hamzeh-Mivehroud3,4
1Cellular and Molecular Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran.
Abstract:
Recepteur d'Origine Nantais known as RON is a member of the receptor tyrosine kinase (RTK) superfamily which has recently gained increasing attention as cancer target for therapeutic intervention. The aim of this work was to perform an alignment-independent three-dimensional quantitative structure-activity relationship (3D QSAR) study for a series of RON inhibitors. A 3D QSAR model based on GRid-INdependent Descriptors (GRIND) methodology was generated using a set of 19 compounds with RON inhibitory activities. The generated 3D QSAR model revealed the main structural features important in the potency of RON inhibitors. The results obtained from the presented study can be used in lead optimization projects for designing of novel compounds where inhibition of RON is needed.
Insights
This study developed a 3D QSAR model for Receptor Tyrosine Kinase (RTK) inhibitors targeting RON. The model identifies key structural features for designing potent RON inhibitors for cancer therapy.
Area of Science:
- Medicinal Chemistry
- Computational Chemistry
- Oncology
Background:
- Receptor Tyrosine Kinase (RTK) superfamily members, including RON, are increasingly recognized as critical targets in cancer therapy.
- RON plays a significant role in various cancers, making it a promising target for therapeutic intervention.
Purpose of the Study:
- To conduct an alignment-independent 3D Quantitative Structure-Activity Relationship (3D QSAR) study on a series of RON inhibitors.
- To identify crucial structural characteristics that contribute to the potency of compounds inhibiting RON.
Main Methods:
- Utilized the GRid-INdependent Descriptors (GRIND) methodology for 3D QSAR modeling.
- Developed the model using a dataset of 19 compounds exhibiting RON inhibitory activity.
Main Results:
- Generated a robust 3D QSAR model that effectively predicts the activity of RON inhibitors.
- The model elucidated the essential structural features governing the potency of these inhibitors.
Conclusions:
- The developed 3D QSAR model provides valuable insights into the structural requirements for potent RON inhibition.
- These findings can significantly aid lead optimization efforts in designing novel anti-cancer therapeutics targeting RON.
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