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RETRACTED ARTICLE: Molecular communication networks: drug target scalability based on artificial intelligence prediction techniques

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This article was retracted on 14 May 2022

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Abstract

With the continuous improvement of computer performance and related technology, the combination of molecular communication networks (MCN) and artificial intelligence (AI) can find the prediction to analyze target methods that are molecular diagnosis (MD) and more efficient. To overcome this issue, researchers have suggested the novel paradigm of the MD that combines nanoparticles-medicine (NPs-M) with tools from synthetic biology to provide re-engineering of biological computing devices. Here, in this work, the prediction model of the target is established by combining the drug calculation model of MCN and deep neural network (DNN) including classification performance for prediction. We developed a numerical analysis model to refer to drug target scalability (DTS), on a genome-wide scale based on protein to protein interaction (PPI) such as in-term of cardiac-disease. Finally, the prediction of the strength and direction of binding affinity between drugs and targets is achieved. Furthermore, to expand the application of the model, with a combination of genetic databases, the micro-processes related to genes are displayed. Then, to combine medicine, a multi-layer network is established based on the effect on/off mechanism. We establish a systematic MCN evaluation model that reveals the basis of MD of medicine and serves for screening, reorientation, drug development, and other fields of medical healthcare industries (MHI). The engineering finding demonstrates that the integration of phenotypic, chemical index in the NPs-M and PPI cannot only achieve scalability but also finds new applications for an existing drug.

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Acknowledgements

We would like to express our great appreciation to the Deep learning laboratory at Harbin Institute of Technology. We also want to thank all the members in laboratories for their technical insights, stimulating ideas, and hard work in data collecting and experimental work, which greatly contributed to the success of our research.

Funding

This research work was supported by the National Natural Science Foundation of China under (No.61100029).

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Authors and Affiliations

  1. School of Control Science and Control Engineering, Harbin Institute of Technology, Harbin, 150001, China

    A. R. Junejo & Xiang Li

  2. School of Computer Science &Technology, Shaanxi Normal University, Xian, China

    Hina Madiha

  3. School of Computer Science & Technology, Harbin Institute of Technology, Harbin, 150001, China

    Soha Mohamed

Authors
  1. A. R. Junejo
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  2. Xiang Li
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  3. Hina Madiha
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  4. Soha Mohamed
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed especially nanoparticles medicine by AR Junejo, and Xiang Li. The first draft of the manuscript was written by Hina Madiha, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to A. R. Junejo.

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This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s11051-022-05483-7

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Junejo, A.R., Li, X., Madiha, H. et al. RETRACTED ARTICLE: Molecular communication networks: drug target scalability based on artificial intelligence prediction techniques. J Nanopart Res 23, 75 (2021). https://doi.org/10.1007/s11051-021-05181-w

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  • DOI: https://doi.org/10.1007/s11051-021-05181-w

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