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Updated: Feb 20, 2026
Screening and Identification of Small Peptides Targeting Fibroblast Growth Factor Receptor2 using a Phage Display Peptide Library
Published on: September 30, 2019
An Alignment-Independent 3D-QSAR Study of FGFR2 Tyrosine Kinase Inhibitors
Behzad Jafari1,2,3, Maryam Hamzeh-Mivehroud1,2, Ali Akbar Alizadeh1
1Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
Abstract:
Receptor tyrosine kinase (RTK) inhibitors are widely used pharmaceuticals in cancer therapy. Fibroblast growth factor receptors (FGFRs) are members of RTK superfamily which are highly expressed on the surface of carcinoma associate fibroblasts (CAFs). The involvement of FGFRs in different types of cancer makes them promising target in cancer therapy and hence, the identification of novel FGFR inhibitors is of great interest. In the current study we aimed to develop an alignment independent three dimensional quantitative structure-activity relationship (3D-QSAR) model for a set of 26 FGFR2 kinase inhibitors allowing the prediction of activity and identification of important structural features for these inhibitors. Pentacle software was used to calculate grid independent descriptors (GRIND) for the active conformers generated by docking followed by the selection of significant variables using fractional factorial design (FFD). The partial least squares (PLS) model generated based on the remaining descriptors was assessed by internal and external validation methods. Six variables were identified as the most important probes-interacting descriptors with high impact on the biological activity of the compounds. Internal and external validations were lead to good statistical parameters (r2 values of 0.93 and 0.665, respectively). The results showed that the model has good predictive power and may be used for designing novel FGFR2 inhibitors.
Insights
Researchers developed a 3D-QSAR model to predict the activity of Fibroblast Growth Factor Receptor 2 (FGFR2) kinase inhibitors. This model aids in designing new FGFR2 inhibitors for cancer therapy by identifying key structural features.
Area of Science:
- Medicinal Chemistry
- Computational Drug Design
- Oncology
Background:
- Receptor tyrosine kinase (RTK) inhibitors are crucial in cancer treatment.
- Fibroblast growth factor receptors (FGFRs), part of the RTK superfamily, are highly expressed on carcinoma-associated fibroblasts (CAFs).
- FGFRs are significant targets in various cancers, driving interest in novel inhibitors.
Purpose of the Study:
- To develop an alignment-independent 3D-QSAR model for 26 FGFR2 kinase inhibitors.
- To predict the biological activity of these inhibitors.
- To identify critical structural features for enhanced FGFR2 inhibitor design.
Main Methods:
- Utilized Pentacle software to compute grid-independent descriptors (GRIND) from docked active conformers.
- Employed fractional factorial design (FFD) for significant variable selection.
- Developed a partial least squares (PLS) model and validated it using internal and external methods.
Main Results:
- Identified six key probe-interacting descriptors significantly impacting biological activity.
- Achieved high statistical performance with internal (r² = 0.93) and external (r² = 0.665) validation.
- The model demonstrated robust predictive capabilities for FGFR2 inhibitor activity.
Conclusions:
- The developed 3D-QSAR model possesses good predictive power for FGFR2 kinase inhibitors.
- The model can effectively guide the rational design of novel and potent FGFR2 inhibitors.
- This approach facilitates the discovery of new therapeutic agents targeting FGFR2 in cancer.
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