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Updated: Jun 10, 2025
A Simple Bioassay for the Evaluation of Vascular Endothelial Growth Factors
Published on: March 15, 2016
Application of Support Vector Machine Classification Model to Identification of Vascular Endothelial Growth Factor
Nooshin Arabi1, Mohammad Reza Torabi2, Fahimeh Ghasemi2
1Department of Bioelectric, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Background:
Nowadays, with the increasing prevalence of cancer mortality, finding the best cancer inhibitors is vital. Angiogenesis, which refers to the formation of new blood vessels from existing ones, undergoes abnormal changes in the physiological process of solid tumors. Vascular endothelial growth factor receptor (VEGFR) plays a crucial role in angiogenesis. Hence, one of the suggestions in cancer treatment has been inhibiting VEGFR signaling to prevent angiogenesis. The computational approach as an in vitro alternative method is crucial to reduce time and cost. This study aimed to use classification algorithm to separate potent inhibitors from inactive ones.
Materials And Methods:
In order to apply the machine learning model, biological compounds were extracted from the BindingDB database. Due to the large number of molecular features, the classification model was susceptible to overfitting. To address this issue, a correlation-based feature selection algorithm was proposed as a means of feature reduction. Subsequently, for the classification step, a support vector machine model that utilizes both linear and non-linear kernels was employed.
Results:
The implementation of the support vector machine model with the radial basis function kernel, along with the correlation-based feature selection method, resulted in a higher accuracy (81.8%, P value = 0.008) compared to other feature selection methods used in this study. Finally, two structures were introduced with the highest binding affinity to inhibit the second VEGFR.
Conclusion:
According to the results, the correlation-based feature selection method is more accurate than other methods.
Insights
This study used machine learning to identify potent cancer inhibitors by targeting vascular endothelial growth factor receptor (VEGFR). A correlation-based feature selection method with a support vector machine model achieved 81.8% accuracy in distinguishing effective inhibitors.
Area of Science:
- Computational chemistry and cheminformatics
- Cancer biology and therapeutics
- Machine learning in drug discovery
Background:
- Cancer mortality necessitates novel therapeutic strategies, with abnormal angiogenesis in tumors being a key target.
- Vascular endothelial growth factor receptor (VEGFR) signaling is critical for tumor angiogenesis, making its inhibition a promising cancer treatment approach.
- Computational methods offer a cost-effective and time-efficient alternative to traditional in vitro screening for identifying potential drug candidates.
Purpose of the Study:
- To develop and apply a machine learning classification model for distinguishing potent VEGFR inhibitors from inactive compounds.
- To utilize computational approaches for accelerating the discovery of novel anti-cancer agents.
Main Methods:
- Biological compounds were sourced from the BindingDB database.
- A correlation-based feature selection algorithm was employed for feature reduction to mitigate overfitting in machine learning models.
- A support vector machine (SVM) model, incorporating both linear and non-linear kernels, was utilized for compound classification.
Main Results:
- The SVM model combined with the correlation-based feature selection and a radial basis function kernel demonstrated superior performance, achieving 81.8% accuracy (P=0.008).
- This approach outperformed other feature selection methods evaluated in the study.
- Two novel chemical structures exhibiting high binding affinity for VEGFR inhibition were identified.
Conclusions:
- The correlation-based feature selection method proved to be a highly accurate approach for feature reduction in this context.
- The study successfully identified potent VEGFR inhibitors using a computational classification strategy.
- The findings support the utility of machine learning in accelerating the identification of targeted cancer therapeutics.
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