Abstract
Introduction: The research and development of drugs, related to the 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.
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: 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.
Conclusion: 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.
Graphical Abstract
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