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Multimodal multi-task deep neural network framework for kinase–target prediction

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Abstract

Kinase plays a significant role in various disease signaling pathways. Due to the highly conserved sequence of kinase family members, understanding the selectivity profile of kinase inhibitors remains a priority for drug discovery. Previous methods for kinase selectivity identification use biochemical assays, which are very useful but limited by the protein available. The lack of kinase selectivity can exert benefits but also can cause adverse effects. With the explosion of the dataset for kinase activities, current computational methods can achieve accuracy for large-scale selectivity predictions. Here, we present a multimodal multi-task deep neural network model for kinase selectivity prediction by calculating the fingerprint and physiochemical descriptors. With the multimodal inputs of structure and physiochemical properties information, the multi-task framework could accurately predict the kinome map for selectivity analysis. The proposed model displays better performance for kinase–target prediction based on system evaluations.

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Abbreviations

DTI:

Drug–target interactions

kNN:

K-nearest neighbor algorithm

MT-DNN:

Multi-task deep neural network

DNN:

Deep neural network

ReLU:

Rectified Linear Unit

MST-DNN:

Multimodal single-task deep neural network

QSAR:

Quantitative structure–activity relationship

MCC:

Matthews correlation coefficient

PCA:

Principal component analysis

CNN:

Convolutional neural network

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Acknowledgements

This work was financially supported by National Natural Science Foundation of China (Grant Nos. 81973182, 82073704, 81803370) State Key Laboratory Innovation Research and Cultivation Fund (Grant No. SKLNMZZCX201812), and “Double World-classes” Construction Program of China Pharmaceutical University (Grant No. CPU2018GF02).

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Authors and Affiliations

  1. Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China

    Yi Hua, Lin Luo, Haodi Qiu, Dingfang Huang, Yang Zhao, Haichun Liu, Tao Lu, Yadong Chen, Yanmin Zhang & Yulei Jiang

  2. State Key Laboratory of Natural Medicines, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, China

    Tao Lu

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  1. Yi Hua
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  2. Lin Luo
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  3. Haodi Qiu
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Correspondence to Yadong Chen, Yanmin Zhang or Yulei Jiang.

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Hua, Y., Luo, L., Qiu, H. et al. Multimodal multi-task deep neural network framework for kinase–target prediction. Mol Divers 27, 2491–2503 (2023). https://doi.org/10.1007/s11030-022-10565-8

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