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Article Open Access

Capturing antibacterial natural products with in silico techniques

  • Authors:
    • Mahmud Masalha
    • Mahmoud Rayan
    • Azmi Adawi
    • Ziyad Abdallah
    • Anwar Rayan
  • View Affiliations / Copyright

    Affiliations: Drug Discovery Informatics Lab, QRC‑Qasemi Research Center, Al‑Qasemi Academic College, Baka EL‑Garbiah 30100, Israel, Institute of Applied Research‑Galilee Society, Shefa‑Amr 20200, Israel
    Copyright: © Masalha et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Pages: 763-770
    |
    Published online on: May 16, 2018
       https://doi.org/10.3892/mmr.2018.9027
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Abstract

The aim of the present study was to index natural products in order to facilitate the discovery of less expensive antibacterial therapeutic drugs. Thus, for modeling purposes, the present study utilized a set of 628 antibacterial drugs, representing the active domain, and 2,892 natural products, representing the inactive domain. In addition, using the iterative stochastic elimination algorithm, 36 unique filters were identified, which were then used to construct a highly discriminative and robust model tailored to index natural products for their antibacterial bioactivity. The area attained under the curve was 0.957, indicating a highly discriminative and robust prediction model. Utilizing the proposed model to virtually screen a mixed set of active and inactive substances enabled the present study to capture 72% of the antibacterial drugs in the top 1% of the sample, yielding an enrichment factor of 72. In total, 10 natural products that scored highly as antibacterial drug candidates with the proposed indexing model were reported. PubMed searches revealed that 2 molecules out of the 10 (caffeine and ricinine) have been tested and identified as showing antibacterial activity. The other 8 phytochemicals await experimental evaluation. Due to the efficiency and rapidity of the proposed prediction model, it could be applied to the virtual screening of large chemical databases to facilitate the drug discovery and development processes for antibacterial drug candidates.
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Copy and paste a formatted citation
Spandidos Publications style
Masalha M, Rayan M, Adawi A, Abdallah Z and Rayan A: Capturing antibacterial natural products with in silico techniques. Mol Med Rep 18: 763-770, 2018.
APA
Masalha, M., Rayan, M., Adawi, A., Abdallah, Z., & Rayan, A. (2018). Capturing antibacterial natural products with in silico techniques. Molecular Medicine Reports, 18, 763-770. https://doi.org/10.3892/mmr.2018.9027
MLA
Masalha, M., Rayan, M., Adawi, A., Abdallah, Z., Rayan, A."Capturing antibacterial natural products with in silico techniques". Molecular Medicine Reports 18.1 (2018): 763-770.
Chicago
Masalha, M., Rayan, M., Adawi, A., Abdallah, Z., Rayan, A."Capturing antibacterial natural products with in silico techniques". Molecular Medicine Reports 18, no. 1 (2018): 763-770. https://doi.org/10.3892/mmr.2018.9027
Copy and paste a formatted citation
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Spandidos Publications style
Masalha M, Rayan M, Adawi A, Abdallah Z and Rayan A: Capturing antibacterial natural products with in silico techniques. Mol Med Rep 18: 763-770, 2018.
APA
Masalha, M., Rayan, M., Adawi, A., Abdallah, Z., & Rayan, A. (2018). Capturing antibacterial natural products with in silico techniques. Molecular Medicine Reports, 18, 763-770. https://doi.org/10.3892/mmr.2018.9027
MLA
Masalha, M., Rayan, M., Adawi, A., Abdallah, Z., Rayan, A."Capturing antibacterial natural products with in silico techniques". Molecular Medicine Reports 18.1 (2018): 763-770.
Chicago
Masalha, M., Rayan, M., Adawi, A., Abdallah, Z., Rayan, A."Capturing antibacterial natural products with in silico techniques". Molecular Medicine Reports 18, no. 1 (2018): 763-770. https://doi.org/10.3892/mmr.2018.9027
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