Identification of Key Features of CNS Drugs Based on SVM and Greedy Algorithm

(E-pub Ahead of Print)

Author(s): Ruilin Zhang, Yanrui Ding*.

Journal Name: Current Computer-Aided Drug Design

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Abstract:

Introduction: The research and development of drugs related to central nervous system (CNS) diseases is a long and arduous process with high cost, long cycle and low success rate. Identification of key features based on available CNS drugs is of great significance for the discovery of new drugs.

Materials and Methods: In this paper, based on the PaDEL descriptors of CNS drugs and non-CNS drugs, a support vector machine (SVM) model was constructed to identify the key features of CNS drugs. Firstly, the random forest algorithm was used to rank descriptors according to the feature significance that contributes to the identification of CNS drugs. Then, a reliable SVM model was constructed, and the optimal combination of descriptors was determined based on greedy algorithm and recursive feature elimination method.

Results and Conclusion: It was found, based on the optimal combination of 40 descriptors, the prediction accuracy of CNS drugs and non-CNS drugs reached 94.2% and 94.4% respectively. nF11HeteroRing, AATSC3v, SpMin6_Bhi, maxdssC, AATS4v, E1v, E3e, GATS5s, minsOH and minHBint4 are the key features to distinguish between CNS drugs and non-CNS drugs.

Keywords: CNS drugs, Support vector machine, Greedy algorithm, Key features, Drug identification, Blood-brain barrier

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Article Details

(E-pub Ahead of Print)
DOI: 10.2174/1573409915666191212095340
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