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Published on: September 30, 2019
Combinatorial Pharmacophore-Based 3D-QSAR Analysis and Virtual Screening of FGFR1 Inhibitors
Nannan Zhou1, Yuan Xu2, Xian Liu3
1State Key Laboratory of Bioreactor Engineering and Shanghai Key Laboratory of Chemical Bilolgy, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China. nannanzhou0912@163.com.
Abstract:
The fibroblast growth factor/fibroblast growth factor receptor (FGF/FGFR) signaling pathway plays crucial roles in cell proliferation, angiogenesis, migration, and survival. Aberration in FGFRs correlates with several malignancies and disorders. FGFRs have proved to be attractive targets for therapeutic intervention in cancer, and it is of high interest to find FGFR inhibitors with novel scaffolds. In this study, a combinatorial three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed based on previously reported FGFR1 inhibitors with diverse structural skeletons. This model was evaluated for its prediction performance on a diverse test set containing 232 FGFR inhibitors, and it yielded a SD value of 0.75 pIC50 units from measured inhibition affinities and a Pearson's correlation coefficient R2 of 0.53. This result suggests that the combinatorial 3D-QSAR model could be used to search for new FGFR1 hit structures and predict their potential activity. To further evaluate the performance of the model, a decoy set validation was used to measure the efficiency of the model by calculating EF (enrichment factor). Based on the combinatorial pharmacophore model, a virtual screening against SPECS database was performed. Nineteen novel active compounds were successfully identified, which provide new chemical starting points for further structural optimization of FGFR1 inhibitors.
Insights
A new 3D-QSAR model identifies novel FGFR1 inhibitors for cancer therapy. This computational approach aids in discovering potential drug candidates by predicting activity and guiding structural optimization.
Area of Science:
- Medicinal Chemistry
- Computational Biology
- Drug Discovery
Background:
- The fibroblast growth factor/fibroblast growth factor receptor (FGF/FGFR) signaling pathway is vital for cellular functions and implicated in various cancers.
- FGFRs are recognized as promising therapeutic targets for cancer treatment, necessitating the development of novel inhibitors with unique chemical structures.
Purpose of the Study:
- To develop and validate a combinatorial 3D-QSAR model for identifying novel FGFR1 inhibitors.
- To utilize the developed model for virtual screening to discover new potential drug candidates.
Main Methods:
- Development of a combinatorial 3D-QSAR model using existing FGFR1 inhibitor data.
- Evaluation of the model's predictive performance on an independent test set of 232 inhibitors.
- Virtual screening of the SPECS database using the validated pharmacophore model.
Main Results:
- The 3D-QSAR model demonstrated predictive capability with a SD of 0.75 pIC50 units and R2 of 0.53.
- Decoy set validation confirmed the model's efficiency in identifying active compounds.
- Virtual screening successfully identified 19 novel active compounds against FGFR1.
Conclusions:
- The combinatorial 3D-QSAR model is a valuable tool for discovering novel FGFR1 inhibitor scaffolds.
- The identified novel compounds serve as promising starting points for further optimization in cancer drug development.
- This study highlights the utility of computational methods in accelerating the identification of targeted cancer therapeutics.
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