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In silico prediction of ROCK II inhibitors by different classification approaches

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Abstract

ROCK II is an important pharmacological target linked to central nervous system disorders such as Alzheimer’s disease. The purpose of this research is to generate ROCK II inhibitor prediction models by machine learning approaches. Firstly, four sets of descriptors were calculated with MOE 2010 and PaDEL-Descriptor, and optimized by F-score and linear forward selection methods. In addition, four classification algorithms were used to initially build 16 classifiers with k-nearest neighbors \((k\hbox {NN})\), naïve Bayes, Random forest, and support vector machine. Furthermore, three sets of structural fingerprint descriptors were introduced to enhance the predictive capacity of classifiers, which were assessed with fivefold cross-validation, test set validation and external test set validation. The best two models, MFK + MACCS and MLR + SubFP, have both MCC values of 0.925 for external test set. After that, a privileged substructure analysis was performed to reveal common chemical features of ROCK II inhibitors. Finally, binding modes were analyzed to identify relationships between molecular descriptors and activity, while main interactions were revealed by comparing the docking interaction of the most potent and the weakest ROCK II inhibitors. To the best of our knowledge, this is the first report on ROCK II inhibitors utilizing machine learning approaches that provides a new method for discovering novel ROCK II inhibitors.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 81273817, 81473740, 81673627), Doctoral Fund of Education Ministry of China (No. 20134425110003), Guangdong Provincial Major Science and Technology for Special Program of China (No. 2012A080202017), the South China Chinese Medicine Collaborative Innovation Center (No. A1-AFD01514A05).

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  1. Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, 12 Jichang Road, Guangzhou, 510405, China

    Chuipu Cai, Qihui Wu, Yunxia Luo, Huili Ma, Jiangang Shen, Lei Yang, Yunbo Chen, Zehuai Wen & Qi Wang

  2. School of Chinese Medicine, The University of Hong Kong, 10 Sassoon Road, Pokfulam, Hong Kong, China

    Jiangang Shen

  3. Laboratory of Experimental Animal, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China

    Yongbin Zhang

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  1. Chuipu Cai
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Cai, C., Wu, Q., Luo, Y. et al. In silico prediction of ROCK II inhibitors by different classification approaches. Mol Divers 21, 791–807 (2017). https://doi.org/10.1007/s11030-017-9772-5

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