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Updated: Nov 12, 2025
Screening and Identification of Small Peptides Targeting Fibroblast Growth Factor Receptor2 using a Phage Display Peptide Library
Published on: September 30, 2019
Ligand based 3D-QSAR model, pharmacophore, molecular docking and ADME to identify potential fibroblast growth factor
Lu Huang1, Xulong Wu2, Xiaoli Fu1
1College of Life Sciences, Sichuan Agricultural University, Ya'an, China.
Abstract:
The FGF/FGFR system may affect tumor cells and stromal microenvironment through autocrine and paracrine stimulation, thereby significantly promoting oncogene transformation and tumor growth. Abnormal expression of FGFR1 in cells is considered to be the main cause of tumorigenesis and a potential target for the treatment of cancer. In this study, a combination of structure-based drug carriers and molecular docking-based virtual screening was used to screen new potential FGFR1 inhibitors. Forty eight known inhibitors were collected to establish 3 D-QSAR models and pharmacophore models, investigate the relationship between the activity and conformation of compounds, and verify the efficiency of pharmacophore. In Accelrys Discovery Studio 2016, the ZINC database was filtered by Lipinski's Rule of Five and SMART's filtration. Then, Hypo01 was used for virtual screening of ZINC database. Compounds with predicted activity values less than 1 μM were molecularly docked with FGFR1 protein crystals, the docking results were observed, and the interaction between compounds and targets was studied. The absorption, distribution, metabolism and excretion (ADME) and toxicity of potential inhibitors were studied, and a compound with new structural scaffolds were obtained. It could be further studied to explore their better therapeutic effects. Communicated by Ramaswamy H. Sarma.
Insights
Researchers identified novel FGFR1 inhibitors for cancer treatment using computational methods. This study combined 3D-QSAR, pharmacophore modeling, and molecular docking to discover new drug candidates targeting FGFR1, a key factor in tumorigenesis.
Area of Science:
- Oncology
- Medicinal Chemistry
- Computational Biology
Background:
- The Fibroblast Growth Factor/Fibroblast Growth Factor Receptor (FGF/FGFR) signaling pathway plays a crucial role in cell growth and differentiation.
- Aberrant FGFR1 expression is implicated in various cancers, making it a promising therapeutic target.
- Understanding the FGF/FGFR system's role in the tumor microenvironment is vital for developing effective cancer treatments.
Purpose of the Study:
- To identify novel inhibitors of Fibroblast Growth Factor Receptor 1 (FGFR1) using a structure-based drug design approach.
- To establish robust 3D-QSAR and pharmacophore models for predicting FGFR1 inhibitor activity.
- To screen and validate potential FGFR1 inhibitors with favorable drug-like properties.
Main Methods:
- Collected 48 known FGFR1 inhibitors to build 3D-QSAR and pharmacophore models.
- Utilized Accelrys Discovery Studio 2016 for virtual screening of the ZINC database.
- Applied Lipinski's Rule of Five and SMART filtration for compound selection.
- Performed molecular docking of potential inhibitors against FGFR1 protein crystals.
- Assessed Absorption, Distribution, Metabolism, and Excretion (ADME) and toxicity profiles.
Main Results:
- Developed and validated 3D-QSAR and pharmacophore models to understand structure-activity relationships.
- Successfully screened the ZINC database, identifying compounds with predicted activity < 1 μM.
- Molecular docking revealed interactions between screened compounds and FGFR1.
- Identified a novel compound with a unique structural scaffold with potential therapeutic effects.
Conclusions:
- The study successfully identified potential FGFR1 inhibitors through integrated computational strategies.
- The developed models provide a foundation for further optimization of FGFR1-targeted cancer therapies.
- The novel compound identified warrants further investigation for its therapeutic efficacy in cancer treatment.
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