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A Methodology for the Prediction of Drug Target Interaction Using CDK Descriptors

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Neural Information Processing (ICONIP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1794))

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Abstract

Detecting probable Drug Target Interaction (DTI) is a critical task in drug discovery. Conventional DTI studies are expensive, labor-intensive, and take a lot of time, hence there are significant reasons to construct useful computational techniques that may successfully anticipate possible DTIs. Although certain methods have been developed for this cause, numerous interactions are yet to be discovered, and prediction accuracy is still low. To meet these challenges, we propose a DTI prediction model built on molecular structure of drugs and sequence of target proteins. In the proposed model, we use Simplified Molecular-Input Line-Entry System (SMILES) to create CDK descriptors, Molecular ACCess System (MACCS) fingerprints, Electrotopological state (Estate) fingerprints and amino-acid sequences of targets to get Pseudo Amino Acid Composition (PseAAC). We target to evaluate performance of DTI prediction models using CDK descriptors. For comparison, we use benchmark data and evaluate models’ performance on two widely used fingerprints, MACCS fingerprints and Estate fingerprints. The evaluation of performances shows that CDK descriptors are superior at predicting DTIs. The proposed method also outperforms other previously published techniques significantly.

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Notes

  1. 1.

    http://www.scbdd.com/chemdes/.

  2. 2.

    https://www.rdkit.org/docs/GettingStartedInPython.html.

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Author information

Authors and Affiliations

  1. Jamia Millia Islamia University, New Delhi, India

    Tanya Liyaqat & Tanvir Ahmad

  2. The Chinese University of Hong Kong, Hong Kong SAR, China

    Chandni Saxena

Authors
  1. Tanya Liyaqat
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  2. Tanvir Ahmad
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  3. Chandni Saxena
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Corresponding author

Correspondence to Tanya Liyaqat .

Editor information

Editors and Affiliations

  1. Indian Institute of Technology Indore, Indore, India

    Mohammad Tanveer

  2. Indian Institute of Information Technology - Allahabad, Prayagraj, India

    Sonali Agarwal

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

  4. Indian Institute of Technology Patna, Patna, India

    Asif Ekbal

  5. University of Innsbruck, Innsbruck, Austria

    Adam Jatowt

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Liyaqat, T., Ahmad, T., Saxena, C. (2023). A Methodology for the Prediction of Drug Target Interaction Using CDK Descriptors. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1794. Springer, Singapore. https://doi.org/10.1007/978-981-99-1648-1_34

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  • DOI: https://doi.org/10.1007/978-981-99-1648-1_34

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  • Online ISBN: 978-981-99-1648-1

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