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Elucidating Quantum Semi-empirical Based QSAR, for Predicting Tannins’ Anti-oxidant Activity with the Help of Artificial Neural Network

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Intelligent Computing Theories and Application (ICIC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13394))

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Abstract

Tannins are potential curatives, besides being an effective antioxidants. Here, tannin based QSAR with machine learning pipeline is elucidated. IC50 values of tannins’ antioxidant activity were adapted from literature. This was further split into training and testing datasets. Furthermore, quantum semi-empirical descriptors were computed. Out of 277 chemical descriptors, 17 were shortlisted by feature selection Multiple Linear Regression. For the test dataset; R2 = 0.706 and mean absolute error (MAE) = 1.94. For the same dataset using nonlinear artificial neural network (ANN), R2 = 0.858 and MAE = 1.02. Therefore, AMPAC-CODESSA’s feature selection and ANN, provides an efficacious tannin-QSAR model aiding tannin-based therapeutic design in future.

C. Gopalakrishnan, C. Xu and Y. Li—Contributed equally.

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Acknowledgement

This study was supported by Provincial Science and Technology Grant of Shanxi Province (20210302124588),Science and technology innovation project of Shanxi province universities (2019L0683). Also, we thank the VIT university and ICMR (File no:5/4–5/Neuro/226/2020/NCD-I) for providing the facilities to carry out this work.

Author information

Authors and Affiliations

  1. Changzhi Medical College, Changzhi, 046000, China

    Caixia Xu & Pengyong Han

  2. Quantitative Biology Lab, Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology (Deemed to be University), 632014, Vellore, Tamil Nadu, India

    Chandrasekhar Gopalakrishnan, Vinutha Anandhan, Sanjay Gangadharan, Meshach Paul, Chandra Sekar Ponnusamy & Rajasekaran Ramalingam

  3. Department of Radiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, China

    Yanran Li

  4. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Zhengwei Li

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  1. Chandrasekhar Gopalakrishnan
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Corresponding authors

Correspondence to Rajasekaran Ramalingam or Pengyong Han .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Xi’an Polytechnic University, Xi’an, China

    Junfeng Jing

  4. The University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  5. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  6. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Gopalakrishnan, C. et al. (2022). Elucidating Quantum Semi-empirical Based QSAR, for Predicting Tannins’ Anti-oxidant Activity with the Help of Artificial Neural Network. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13394. Springer, Cham. https://doi.org/10.1007/978-3-031-13829-4_24

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  • DOI: https://doi.org/10.1007/978-3-031-13829-4_24

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