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DTIFS: A Novel Computational Approach for Predicting Drug-Target Interactions from Drug Structure and Protein Sequence

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Intelligent Computing Theories and Application (ICIC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12464))

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Abstract

Identification and prediction of Drug-Target Interactions (DTIs) is the basis for screening drug candidates, which plays a vital role in the development of innovative drugs. However, due to the time-consuming and high cost constraints of biological experimental methods, traditional drug target identification technologies are often difficult to develop on a large scale. Therefore, in silico methods are urgently needed to predict drug-target interactions in a genome-wide manner. In this article, we design a novel in silico approach, named DTIFS to predict the DTIs by combining Feature weighted Rotation Forest (FwRF) classifier with protein amino acids information. This model has two outstanding advantages: a) using the fusion data of protein sequence and drug molecular fingerprint, which can fully carry information; b) using the classifier with feature selection ability, which can effectively remove noise information and improve prediction performance. More specifically, we first use Position-Specific Score Matrix (PSSM) to numerically convert protein sequences and utilize Pseudo Position-Specific Score Matrix (PsePSSM) to extract their features. Then a unified digital descriptor is formed by combining molecular fingerprints representing drug information. Finally, the FwRF is applied to implement on Enzyme, Ion Channel, GPCR, and Nuclear Receptor datasets. The results of the 5-fold CV experiment show that the prediction accuracy of this approach reaches 91.68%, 88.11%, 84.72% and 78.33% on four benchmark datasets, respectively. To further validate the performance of the DTIFS, we compare it with other excellent methods and Support Vector Machine (SVM) model. The experimental results of cross-validation indicated that DTIFS is feasible in predicting the relationship among drugs and target, and can provide help for the discovery of new candidate drugs.

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China, under Grants 61702444, in part by the West Light Foundation of The Chinese Academy of Sciences, under Grant 2018-XBQNXZ-B-008, in part by the Chinese Postdoctoral Science Foundation, under Grant 2019M653804, in part by the Tianshan youth - Excellent Youth, under Grant 2019Q029, in part by the Qingtan scholar talent project of Zaozhuang University. The authors would like to thank all anonymous reviewers for their constructive advices.

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China

    Xin Yan & Kai Zheng

  2. College of Information Science and Engineering, Zaozhuang University, Zaozhuang, China

    Xin Yan & Lei Wang

  3. Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China

    Zhu-Hong You, Lei Wang & Li-Ping Li

  4. School of Mathematics and Computer Science, Yichun University, Yichun, 336000, China

    Mei-Neng Wang

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  1. Xin Yan
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  2. Zhu-Hong You
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  3. Lei Wang
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  4. Li-Ping Li
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  5. Kai Zheng
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  6. Mei-Neng Wang
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Correspondence to Zhu-Hong You or Lei Wang .

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Editors and Affiliations

  1. Institute of Machine Learning and Systems Biology, Tongji University, Shanghai, China

    De-Shuang Huang

  2. School of Electrical Engineering, University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

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Yan, X., You, ZH., Wang, L., Li, LP., Zheng, K., Wang, MN. (2020). DTIFS: A Novel Computational Approach for Predicting Drug-Target Interactions from Drug Structure and Protein Sequence. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12464. Springer, Cham. https://doi.org/10.1007/978-3-030-60802-6_33

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  • DOI: https://doi.org/10.1007/978-3-030-60802-6_33

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-60802-6

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