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Asmare MM, Nitin N, Yun SI, et al. QSAR and deep learning model for virtual screening of potential inhibitors against Inosine 5' Monophosphate dehydrogenase (IMPDH) of Cryptosporidium parvum. J Mol Graph Model. 2021;111:108108doi: 10.1016/j.jmgm.2021.108108.
Asmare, M. M., Nitin, N., Yun, S. I., & Mahapatra, R. K. (2021). QSAR and deep learning model for virtual screening of potential inhibitors against Inosine 5' Monophosphate dehydrogenase (IMPDH) of Cryptosporidium parvum. Journal of molecular graphics & modelling, 111108108. https://doi.org/10.1016/j.jmgm.2021.108108
Asmare, Misgana Mengistu, et al. "QSAR and deep learning model for virtual screening of potential inhibitors against Inosine 5' Monophosphate dehydrogenase (IMPDH) of Cryptosporidium parvum." Journal of molecular graphics & modelling vol. 111 (2021): 108108. doi: https://doi.org/10.1016/j.jmgm.2021.108108
Asmare MM, Nitin N, Yun SI, Mahapatra RK. QSAR and deep learning model for virtual screening of potential inhibitors against Inosine 5' Monophosphate dehydrogenase (IMPDH) of Cryptosporidium parvum. J Mol Graph Model. 2021 Dec 10;111:108108. doi: 10.1016/j.jmgm.2021.108108. Epub 2021 Dec 10. PMID: 34911011.
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J Mol Graph Model. 2021 Dec 10;111:108108. doi: 10.1016/j.jmgm.2021.108108. Epub 2021 Dec 10.

QSAR and deep learning model for virtual screening of potential inhibitors against Inosine 5' Monophosphate dehydrogenase (IMPDH) of Cryptosporidium parvum.

Journal of molecular graphics & modelling

Misgana Mengistu Asmare, Nitin Nitin, Soon-Il Yun, Rajani Kanta Mahapatra

Affiliations

  1. School of Biotechnology, KIIT Deemed to be University, Bhubaneswar, 751024, Odisha, India.
  2. Department of Food Science and Technology, University of California, Davis, Davis, CA, USA.
  3. Department of Food Science and Technology, Jeonbuk National University, Jeonju, 54896, Republic of Korea; Department of Agricultural Convergence Technology, Jeonbuk National University, Jeonju, 54896, Republic of Korea. Electronic address: [email protected].
  4. School of Biotechnology, KIIT Deemed to be University, Bhubaneswar, 751024, Odisha, India. Electronic address: [email protected].

PMID: 34911011 DOI: 10.1016/j.jmgm.2021.108108

Abstract

Cryptosporidium parvum (Cp) causes a gastro-intestinal disease called Cryptosporidiosis. C. parvum Inosine 5' monophosphate dehydrogenase (CpIMPDH) is responsible for the production of guanine nucleotides. In the present study, 37 known urea-based congeneric compounds were used to build a 2D and 3D QSAR model against CpIMPDH. The built models were validated based on OECD principles. A deep learning model was adopted from a framework called Deep Purpose. The model was trained with 288 known active compounds and validated using a test set. From the training set of the 3D QSAR, a pharmacophore model was built and the best pharmacophore hypotheses were scored and sorted using a phase-hypo score. A phytochemical database was screened using both the pharmacophore model and a deep learning model. The screened compounds were considered for glide XP docking, followed by quantum polarized ligand docking. Finally, the best compound among them was considered for molecular dynamics simulation study.

Copyright © 2021 Elsevier Inc. All rights reserved.

Keywords: 2D and 3D QSAR; CpIMPDH; Deep learning; MD simulation; Phramcophore

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