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.

引言：中枢神经系统疾病相关药物的研发是一个漫长而艰巨的过程，成本高、周期长、成功率低。基于现有中枢神经系统药物的关键特征识别对于新药的发现具有重要意义。

方法：在本文中，基于中枢神经系统药物和非中枢神经系统药物的 PaDEL 描述符，构建支持向量机（SVM）模型来识别中枢神经系统药物的关键特征。首先，使用随机森林算法根据有助于识别中枢神经系统药物的特征重要性对描述符进行排序。然后，构建可靠的SVM模型，并基于贪心算法和递归特征消除方法确定描述符的最佳组合。

结果：发现基于40个描述符的最佳组合，CNS药物和非CNS药物的预测准确率分别达到94.2%和94.4%。

结论：nF11HeteroRing、AATSC3v、SpMin6_Bhi、maxdssC、AATS4v、E1v、E3e、GATS5s、minsOH和minHBint4是区分CNS药物和非CNS药物的关键特征。