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Improving the Prediction of Potential Kinase Inhibitors with Feature Learning on Multisource Knowledge

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Interdisciplinary Sciences: Computational Life Sciences Aims and scope Submit manuscript

Abstract

Purpose

The identification of potential kinase inhibitors plays a key role in drug discovery for treating human diseases. Currently, most existing computational methods only extract limited features such as sequence information from kinases and inhibitors. To further enhance the identification of kinase inhibitors, more features need to be leveraged. Hence, it is appealing to develop effective methods to aggregate feature information from multisource knowledge for predicting potential kinase inhibitors. In this paper, we propose a novel computational framework called FLMTS to improve the performance of kinase inhibitor prediction by aggregating multisource knowledge.

Method

FLMTS uses a random walk with restart (RWR) to combine multiscale information in a heterogeneous network. We used the combined information as features of compounds and kinases and input them into random forest (RF) to predict unknown compound–kinase interactions.

Results

Experimental results reveal that FLMTS obtains significant improvement over existing state-of-the-art methods. Case studies demonstrated the reliability of FLMTS, and pathway enrichment analysis demonstrated that FLMTS could also accurately predict signaling pathways in disease treatment.

Conclusion

In conclusion, our computational framework of FLMTS for improving the prediction of potential kinase inhibitors successfully aggregates feature information from multisource knowledge, yielding better prediction performance than existing state-of-the-art methods.

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Data Availability

Source code and the dataset supporting the conclusions of this article are available from the corresponding author upon reasonable request.

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Acknowledgements

This work was supported in part by the Hunan Provincial Natural Science Foundation of China (No. 2019JJ50520), Hunan Provincial Innovation Foundation For Postgraduate (No. CX20210937).

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Authors and Affiliations

  1. School of Computer Science, University of South China, Hengyang, 421001, China

    Yichen Zhong, Huanhuan Wu, Tao Xu & Lingyun Luo

  2. Hunan Provincial Base for Scientific and Technological Innovation Cooperation, Hengyang, 421001, China

    Yichen Zhong & Lingyun Luo

  3. College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410083, China

    Cong Shen

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  1. Yichen Zhong
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Correspondence to Lingyun Luo.

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Zhong, Y., Shen, C., Wu, H. et al. Improving the Prediction of Potential Kinase Inhibitors with Feature Learning on Multisource Knowledge. Interdiscip Sci Comput Life Sci 14, 775–785 (2022). https://doi.org/10.1007/s12539-022-00523-1

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