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A review of deep learning algorithms for modeling drug interactions

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Abstract

The interactions between therapeutics and their targets are an important part of the drug development process. To counter the cost, time and accuracy related issues, novel and efficient DL algorithms are required. These approaches have proven successful in quickly identifying and predicting possible drug interactions. Here, we examine computational strategies for predicting drug interactions in the context of drug development, focusing on artificial intelligence-based approaches. We start by providing a succinct overview of deep learning in drug development and drug interactions. Next, we review and evaluate AI-based methods used to forecast Drug–Target Interactions, drug–drug interactions, drug–disease interactions, and ploy-pharmacy side effects, including both sequential and graph-based modern DL algorithms. Lastly, we examine databases with their brief description and sources that are frequently utilized to research drug interactions.

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Acknowledgements

This work was funded by JKSTIC DST.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Islamic University of Science and Technology, Awantipora, India

    Aga Basit Iqbal, Idris Afzal Shah, Assif Assad & Syed Zubair Shah

  2. Department of Electronics and Communication Engineering, National Institute of Technology, Srinagar, India

    Injila

  3. Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur, India

    Mushtaq Ahmed

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  1. Aga Basit Iqbal
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  2. Idris Afzal Shah
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  3. Injila
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Basit, Idris, Injila wrote the main manuscript. Zubair and Assif prepared tabular data. Mushtaq critically reviewed it.

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Iqbal, A.B., Shah, I.A., Injila et al. A review of deep learning algorithms for modeling drug interactions. Multimedia Systems 30, 124 (2024). https://doi.org/10.1007/s00530-024-01325-9

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