International Conference on Information and Knowledge Technology

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Enhancing QSAR Modeling: A Fusion of Sequential Feature Selection and Support Vector Machine

Authors :

Farzaneh Khajehgili-Mirabadi¹ Mohammad Reza Keyvanpour²

1- دانشگاه الزهرا(س) 2- دانشگاه الزهرا(س)

Keywords :

Descriptors selection،Drug discovery،QSAR modeling،Sequential Feature Selection،Support vector machine

Abstract :

Quantitative Structure-Activity Relationship (QSAR) modeling is an approach employed to predict the biological response of chemical compounds by considering their structural attributes. Classification machine learning algorithms can learn patterns and relationships between chemical structure (descriptors) and biological activity from datasets and then use this knowledge to predict active or inactive compounds. This study introduces a new approach that combines Sequential Feature Selection (SFS) with the Support Vector Machine (SVM) algorithm to select the most relevant molecular descriptors for QSAR modeling. SFS and SVM work collaboratively to identify the best subset of descriptors, resulting in improved predictive accuracy. The key steps include selecting an appropriate subset of descriptors using SFS from a larger set, SVM models are built using different subsets of descriptors, and the most accurate model is selected for final use. As shown by measuring Accuracy, Precision, Recall, and F1-score of the proposed SVM algorithm in two datasets, DKPES and PubChem, The results demonstrate the effectiveness and robustness of this approach in achieving subsets of descriptors with strong predictive capabilities.