Capturing antibacterial natural products with in silico techniques
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, rep...
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| Veröffentlicht in: | Molecular medicine reports 2018-07, Vol.18 (1), p.763-770 |
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| creator | Masalha, Mahmud Rayan, Mahmoud Adawi, Azmi Abdallah, Ziyad Rayan, Anwar |
| description | 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. |
| doi_str_mv | 10.3892/mmr.2018.9027 |
| format | Article |
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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.</description><identifier>ISSN: 1791-2997</identifier><identifier>EISSN: 1791-3004</identifier><identifier>DOI: 10.3892/mmr.2018.9027</identifier><identifier>PMID: 29845192</identifier><language>eng</language><publisher>Greece: Spandidos Publications</publisher><subject>Algorithms ; Antibacterial activity ; Antibacterial agents ; Antibiotics ; Antimicrobial agents ; Bacteria ; Biological activity ; Caffeine ; Chemicals ; Drug development ; Drug discovery ; Drug resistance ; Efficiency ; FDA approval ; Identification and classification ; Ligands ; Natural products ; Nosocomial infections ; Pharmaceutical industry ; Pharmaceutical research ; Phytochemicals ; R&D ; Research & development ; Stochasticity ; Testing</subject><ispartof>Molecular medicine reports, 2018-07, Vol.18 (1), p.763-770</ispartof><rights>COPYRIGHT 2018 Spandidos Publications</rights><rights>Copyright Spandidos Publications UK Ltd. 2018</rights><rights>Copyright: © Masalha et al. 2018</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29845192$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Masalha, Mahmud</creatorcontrib><creatorcontrib>Rayan, Mahmoud</creatorcontrib><creatorcontrib>Adawi, Azmi</creatorcontrib><creatorcontrib>Abdallah, Ziyad</creatorcontrib><creatorcontrib>Rayan, Anwar</creatorcontrib><title>Capturing antibacterial natural products with in silico techniques</title><title>Molecular medicine reports</title><addtitle>Mol Med Rep</addtitle><description>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.</description><subject>Algorithms</subject><subject>Antibacterial activity</subject><subject>Antibacterial agents</subject><subject>Antibiotics</subject><subject>Antimicrobial agents</subject><subject>Bacteria</subject><subject>Biological activity</subject><subject>Caffeine</subject><subject>Chemicals</subject><subject>Drug development</subject><subject>Drug discovery</subject><subject>Drug resistance</subject><subject>Efficiency</subject><subject>FDA approval</subject><subject>Identification and classification</subject><subject>Ligands</subject><subject>Natural products</subject><subject>Nosocomial infections</subject><subject>Pharmaceutical industry</subject><subject>Pharmaceutical research</subject><subject>Phytochemicals</subject><subject>R&D</subject><subject>Research & development</subject><subject>Stochasticity</subject><subject>Testing</subject><issn>1791-2997</issn><issn>1791-3004</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNptkUtLxDAQx4Movo9epeDFy66TV5NcBFl8geBFzyFN090sbbq2qeK38bP4yczi-liRHCbM_PKf-U8QOsIwplKRs6bpxgSwHCsgYgPtYqHwiAKwzdWdKCV20F7fzwFyTrjaRjtEScaxIrtoMjGLOHQ-TDMToi-Mja7zps6CSekUF11bDjb22YuPs8yH97fe1962WXR2FvzT4PoDtFWZuneHq7iPHq8uHyY3o7v769vJxd1oSnMcR7SS1BFHmZJCKFKSsjAgSwoU5-BEYQm2OZZYcGaMdVZZbCxUBVBFucCE7qPzT93FUDSutC7ENKFedL4x3atujdfrleBneto-6xy4EsCSwOlKoGuXg0fd-N66ujbBtUOvCTBBpGRYJvTkDzpvhy4ke4lSnFEOgvxQU1M77UPVpr52KaovOCeSMQEqUeN_qHRK16RNBlf5lF97cPzb6LfDr2-jH1IAmWo</recordid><startdate>20180701</startdate><enddate>20180701</enddate><creator>Masalha, Mahmud</creator><creator>Rayan, Mahmoud</creator><creator>Adawi, Azmi</creator><creator>Abdallah, Ziyad</creator><creator>Rayan, Anwar</creator><general>Spandidos Publications</general><general>Spandidos Publications UK Ltd</general><general>D.A. 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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.</abstract><cop>Greece</cop><pub>Spandidos Publications</pub><pmid>29845192</pmid><doi>10.3892/mmr.2018.9027</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Antibacterial activity Antibacterial agents Antibiotics Antimicrobial agents Bacteria Biological activity Caffeine Chemicals Drug development Drug discovery Drug resistance Efficiency FDA approval Identification and classification Ligands Natural products Nosocomial infections Pharmaceutical industry Pharmaceutical research Phytochemicals R&D Research & development Stochasticity Testing |
| title | Capturing antibacterial natural products with in silico techniques |
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