Prediction of Anti‐Alzheimer's Activity of Flavonoids Targeting Acetylcholinesterase in silico
Introduction Prenylated and pyrano‐flavonoids of the genus Artocarpus J. R. Forster & G. Forster are well known for their acetylcholinesterase (AChE) inhibitory, anti‐cholinergic, anti‐inflammatory, anti‐microbial, anti‐oxidant, anti‐proliferative and tyrosinase inhibitory activities. Some of th...
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| Veröffentlicht in: | Phytochemical analysis 2017-07, Vol.28 (4), p.324-331 |
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| creator | Das, Subrata Laskar, Monjur A. Sarker, Satyajit D. Choudhury, Manabendra D. Choudhury, Prakash Roy Mitra, Abhijit Jamil, Shajarahtunnur Lathiff, Siti Mariam A. Abdullah, Siti Awanis Basar, Norazah Nahar, Lutfun Talukdar, Anupam D. |
| description | Introduction
Prenylated and pyrano‐flavonoids of the genus Artocarpus J. R. Forster & G. Forster are well known for their acetylcholinesterase (AChE) inhibitory, anti‐cholinergic, anti‐inflammatory, anti‐microbial, anti‐oxidant, anti‐proliferative and tyrosinase inhibitory activities. Some of these compounds have also been shown to be effective against Alzheimer's disease.
Objective
The aim of the in silico study was to establish protocols to predict the most effective flavonoid from prenylated and pyrano‐flavonoid classes for AChE inhibition linking to the potential treatment of Alzheimer's disease.
Methodology
Three flavonoids isolated from Artocarpus anisophyllus Miq. were selected for the study. With these compounds, Lipinski filter, ADME/Tox screening, molecular docking and quantitative structure–activity relationship (QSAR) were performed in silico. In vitro activity was evaluated by bioactivity staining based on the Ellman's method.
Results
In the Lipinski filter and ADME/Tox screening, all test compounds produced positive results, but in the target fishing, only one flavonoid could successfully target AChE. Molecular docking was performed on this flavonoid, and this compound gained the score as ˗13.5762. From the QSAR analysis the IC50 was found to be 1659.59 nM. Again, 100 derivatives were generated from the parent compound and docking was performed. The derivative compound 20 was the best scorer, i.e. ˗31.6392 and IC50 was predicted as 6.025 nM.
Conclusion
Results indicated that flavonoids could be efficient inhibitors of AChE and thus, could be useful in the management of Alzheimer's disease. Copyright © 2017 John Wiley & Sons, Ltd.
The authors established protocols to predict the anti‐Alzheimer's activity of flavonoids targeting acetylcholinesterase by the in silico approach. The key finding is that, the 5, 7 Dihydroxy‐4'‐methoxy‐8‐prenylflavanone a flavonoid could be an efficient inhibitor of AchE and thus, could be useful in the management of Alzheimer's disease. |
| doi_str_mv | 10.1002/pca.2679 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1865819837</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1908804579</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3839-4d2c3c3964499edcc1be06b8f36b58fed30f3418e2337803aea2431c080ee7e63</originalsourceid><addsrcrecordid>eNp1kM1KAzEURoMotv6ATyADLnQzNZnMZJLlUKwKgl3oOqaZOxpJJzWZVurKR_AZfRJTrQqCq7v4DofLQeiA4AHBODudaTXIWCk2UJ9gIVJSsGIT9bEoeIppnvfQTgiPGMdNsG3UyzhhvGRFH92NPdRGd8a1iWuSqu3M--tbZV8ewEzBH4ekiuPCdMvVPLJq4Vpn6pDcKH8PnWnvIwDd0uoHZ00LoQOvAiSmTYKxRrs9tNUoG2B_fXfR7ejsZniRXl2fXw6rq1RTTkWa15mmmgqW50JArTWZAGYT3lA2KXgDNcUNzQmHjNKSY6pAZTklGnMMUAKju-jkyzvz7mke_5BTEzRYq1pw8yAJZwUngtMyokd_0Ec39238ThKBOcd5UYpfofYuBA-NnHkzVX4pCZar6jJWl6vqET1cC-eTKdQ_4HfmCKRfwLOxsPxXJMfD6lP4AcAojDg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1908804579</pqid></control><display><type>article</type><title>Prediction of Anti‐Alzheimer's Activity of Flavonoids Targeting Acetylcholinesterase in silico</title><source>MEDLINE</source><source>Wiley Online Library Journals Frontfile Complete</source><creator>Das, Subrata ; Laskar, Monjur A. ; Sarker, Satyajit D. ; Choudhury, Manabendra D. ; Choudhury, Prakash Roy ; Mitra, Abhijit ; Jamil, Shajarahtunnur ; Lathiff, Siti Mariam A. ; Abdullah, Siti Awanis ; Basar, Norazah ; Nahar, Lutfun ; Talukdar, Anupam D.</creator><creatorcontrib>Das, Subrata ; Laskar, Monjur A. ; Sarker, Satyajit D. ; Choudhury, Manabendra D. ; Choudhury, Prakash Roy ; Mitra, Abhijit ; Jamil, Shajarahtunnur ; Lathiff, Siti Mariam A. ; Abdullah, Siti Awanis ; Basar, Norazah ; Nahar, Lutfun ; Talukdar, Anupam D.</creatorcontrib><description>Introduction
Prenylated and pyrano‐flavonoids of the genus Artocarpus J. R. Forster & G. Forster are well known for their acetylcholinesterase (AChE) inhibitory, anti‐cholinergic, anti‐inflammatory, anti‐microbial, anti‐oxidant, anti‐proliferative and tyrosinase inhibitory activities. Some of these compounds have also been shown to be effective against Alzheimer's disease.
Objective
The aim of the in silico study was to establish protocols to predict the most effective flavonoid from prenylated and pyrano‐flavonoid classes for AChE inhibition linking to the potential treatment of Alzheimer's disease.
Methodology
Three flavonoids isolated from Artocarpus anisophyllus Miq. were selected for the study. With these compounds, Lipinski filter, ADME/Tox screening, molecular docking and quantitative structure–activity relationship (QSAR) were performed in silico. In vitro activity was evaluated by bioactivity staining based on the Ellman's method.
Results
In the Lipinski filter and ADME/Tox screening, all test compounds produced positive results, but in the target fishing, only one flavonoid could successfully target AChE. Molecular docking was performed on this flavonoid, and this compound gained the score as ˗13.5762. From the QSAR analysis the IC50 was found to be 1659.59 nM. Again, 100 derivatives were generated from the parent compound and docking was performed. The derivative compound 20 was the best scorer, i.e. ˗31.6392 and IC50 was predicted as 6.025 nM.
Conclusion
Results indicated that flavonoids could be efficient inhibitors of AChE and thus, could be useful in the management of Alzheimer's disease. Copyright © 2017 John Wiley & Sons, Ltd.
The authors established protocols to predict the anti‐Alzheimer's activity of flavonoids targeting acetylcholinesterase by the in silico approach. The key finding is that, the 5, 7 Dihydroxy‐4'‐methoxy‐8‐prenylflavanone a flavonoid could be an efficient inhibitor of AchE and thus, could be useful in the management of Alzheimer's disease.</description><identifier>ISSN: 0958-0344</identifier><identifier>EISSN: 1099-1565</identifier><identifier>DOI: 10.1002/pca.2679</identifier><identifier>PMID: 28168765</identifier><language>eng</language><publisher>England: Wiley Subscription Services, Inc</publisher><subject>Acetylcholinesterase ; ADME/Tox screening ; Alzheimer Disease - drug therapy ; Alzheimer's disease ; Antiinfectives and antibacterials ; Artocarpus anisophyllus ; Biochemistry ; Biocompatibility ; Biomedical materials ; Cholinesterase Inhibitors - pharmacology ; Derivatives ; Disease control ; Flavonoids ; Flavonoids - pharmacology ; Humans ; In vitro methods and tests ; Inflammation ; Inhibitors ; Lipinski filter ; Management ; Microorganisms ; Molecular docking ; Molecular Docking Simulation ; Molecular structure ; Neurodegenerative diseases ; Oxidizing agents ; Predictions ; QSAR ; Quantitative Structure-Activity Relationship ; Screening ; Staining ; Structure-activity relationships ; Surgical implants ; Therapeutic applications</subject><ispartof>Phytochemical analysis, 2017-07, Vol.28 (4), p.324-331</ispartof><rights>Copyright © 2017 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3839-4d2c3c3964499edcc1be06b8f36b58fed30f3418e2337803aea2431c080ee7e63</citedby><cites>FETCH-LOGICAL-c3839-4d2c3c3964499edcc1be06b8f36b58fed30f3418e2337803aea2431c080ee7e63</cites><orcidid>0000-0001-8916-2791</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fpca.2679$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fpca.2679$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1416,27915,27916,45565,45566</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28168765$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Das, Subrata</creatorcontrib><creatorcontrib>Laskar, Monjur A.</creatorcontrib><creatorcontrib>Sarker, Satyajit D.</creatorcontrib><creatorcontrib>Choudhury, Manabendra D.</creatorcontrib><creatorcontrib>Choudhury, Prakash Roy</creatorcontrib><creatorcontrib>Mitra, Abhijit</creatorcontrib><creatorcontrib>Jamil, Shajarahtunnur</creatorcontrib><creatorcontrib>Lathiff, Siti Mariam A.</creatorcontrib><creatorcontrib>Abdullah, Siti Awanis</creatorcontrib><creatorcontrib>Basar, Norazah</creatorcontrib><creatorcontrib>Nahar, Lutfun</creatorcontrib><creatorcontrib>Talukdar, Anupam D.</creatorcontrib><title>Prediction of Anti‐Alzheimer's Activity of Flavonoids Targeting Acetylcholinesterase in silico</title><title>Phytochemical analysis</title><addtitle>Phytochem Anal</addtitle><description>Introduction
Prenylated and pyrano‐flavonoids of the genus Artocarpus J. R. Forster & G. Forster are well known for their acetylcholinesterase (AChE) inhibitory, anti‐cholinergic, anti‐inflammatory, anti‐microbial, anti‐oxidant, anti‐proliferative and tyrosinase inhibitory activities. Some of these compounds have also been shown to be effective against Alzheimer's disease.
Objective
The aim of the in silico study was to establish protocols to predict the most effective flavonoid from prenylated and pyrano‐flavonoid classes for AChE inhibition linking to the potential treatment of Alzheimer's disease.
Methodology
Three flavonoids isolated from Artocarpus anisophyllus Miq. were selected for the study. With these compounds, Lipinski filter, ADME/Tox screening, molecular docking and quantitative structure–activity relationship (QSAR) were performed in silico. In vitro activity was evaluated by bioactivity staining based on the Ellman's method.
Results
In the Lipinski filter and ADME/Tox screening, all test compounds produced positive results, but in the target fishing, only one flavonoid could successfully target AChE. Molecular docking was performed on this flavonoid, and this compound gained the score as ˗13.5762. From the QSAR analysis the IC50 was found to be 1659.59 nM. Again, 100 derivatives were generated from the parent compound and docking was performed. The derivative compound 20 was the best scorer, i.e. ˗31.6392 and IC50 was predicted as 6.025 nM.
Conclusion
Results indicated that flavonoids could be efficient inhibitors of AChE and thus, could be useful in the management of Alzheimer's disease. Copyright © 2017 John Wiley & Sons, Ltd.
The authors established protocols to predict the anti‐Alzheimer's activity of flavonoids targeting acetylcholinesterase by the in silico approach. The key finding is that, the 5, 7 Dihydroxy‐4'‐methoxy‐8‐prenylflavanone a flavonoid could be an efficient inhibitor of AchE and thus, could be useful in the management of Alzheimer's disease.</description><subject>Acetylcholinesterase</subject><subject>ADME/Tox screening</subject><subject>Alzheimer Disease - drug therapy</subject><subject>Alzheimer's disease</subject><subject>Antiinfectives and antibacterials</subject><subject>Artocarpus anisophyllus</subject><subject>Biochemistry</subject><subject>Biocompatibility</subject><subject>Biomedical materials</subject><subject>Cholinesterase Inhibitors - pharmacology</subject><subject>Derivatives</subject><subject>Disease control</subject><subject>Flavonoids</subject><subject>Flavonoids - pharmacology</subject><subject>Humans</subject><subject>In vitro methods and tests</subject><subject>Inflammation</subject><subject>Inhibitors</subject><subject>Lipinski filter</subject><subject>Management</subject><subject>Microorganisms</subject><subject>Molecular docking</subject><subject>Molecular Docking Simulation</subject><subject>Molecular structure</subject><subject>Neurodegenerative diseases</subject><subject>Oxidizing agents</subject><subject>Predictions</subject><subject>QSAR</subject><subject>Quantitative Structure-Activity Relationship</subject><subject>Screening</subject><subject>Staining</subject><subject>Structure-activity relationships</subject><subject>Surgical implants</subject><subject>Therapeutic applications</subject><issn>0958-0344</issn><issn>1099-1565</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kM1KAzEURoMotv6ATyADLnQzNZnMZJLlUKwKgl3oOqaZOxpJJzWZVurKR_AZfRJTrQqCq7v4DofLQeiA4AHBODudaTXIWCk2UJ9gIVJSsGIT9bEoeIppnvfQTgiPGMdNsG3UyzhhvGRFH92NPdRGd8a1iWuSqu3M--tbZV8ewEzBH4ekiuPCdMvVPLJq4Vpn6pDcKH8PnWnvIwDd0uoHZ00LoQOvAiSmTYKxRrs9tNUoG2B_fXfR7ejsZniRXl2fXw6rq1RTTkWa15mmmgqW50JArTWZAGYT3lA2KXgDNcUNzQmHjNKSY6pAZTklGnMMUAKju-jkyzvz7mke_5BTEzRYq1pw8yAJZwUngtMyokd_0Ec39238ThKBOcd5UYpfofYuBA-NnHkzVX4pCZar6jJWl6vqET1cC-eTKdQ_4HfmCKRfwLOxsPxXJMfD6lP4AcAojDg</recordid><startdate>201707</startdate><enddate>201707</enddate><creator>Das, Subrata</creator><creator>Laskar, Monjur A.</creator><creator>Sarker, Satyajit D.</creator><creator>Choudhury, Manabendra D.</creator><creator>Choudhury, Prakash Roy</creator><creator>Mitra, Abhijit</creator><creator>Jamil, Shajarahtunnur</creator><creator>Lathiff, Siti Mariam A.</creator><creator>Abdullah, Siti Awanis</creator><creator>Basar, Norazah</creator><creator>Nahar, Lutfun</creator><creator>Talukdar, Anupam D.</creator><general>Wiley Subscription Services, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QL</scope><scope>7QR</scope><scope>7T7</scope><scope>7TM</scope><scope>7U9</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H94</scope><scope>K9.</scope><scope>M7N</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-8916-2791</orcidid></search><sort><creationdate>201707</creationdate><title>Prediction of Anti‐Alzheimer's Activity of Flavonoids Targeting Acetylcholinesterase in silico</title><author>Das, Subrata ; Laskar, Monjur A. ; Sarker, Satyajit D. ; Choudhury, Manabendra D. ; Choudhury, Prakash Roy ; Mitra, Abhijit ; Jamil, Shajarahtunnur ; Lathiff, Siti Mariam A. ; Abdullah, Siti Awanis ; Basar, Norazah ; Nahar, Lutfun ; Talukdar, Anupam D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3839-4d2c3c3964499edcc1be06b8f36b58fed30f3418e2337803aea2431c080ee7e63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Acetylcholinesterase</topic><topic>ADME/Tox screening</topic><topic>Alzheimer Disease - drug therapy</topic><topic>Alzheimer's disease</topic><topic>Antiinfectives and antibacterials</topic><topic>Artocarpus anisophyllus</topic><topic>Biochemistry</topic><topic>Biocompatibility</topic><topic>Biomedical materials</topic><topic>Cholinesterase Inhibitors - pharmacology</topic><topic>Derivatives</topic><topic>Disease control</topic><topic>Flavonoids</topic><topic>Flavonoids - pharmacology</topic><topic>Humans</topic><topic>In vitro methods and tests</topic><topic>Inflammation</topic><topic>Inhibitors</topic><topic>Lipinski filter</topic><topic>Management</topic><topic>Microorganisms</topic><topic>Molecular docking</topic><topic>Molecular Docking Simulation</topic><topic>Molecular structure</topic><topic>Neurodegenerative diseases</topic><topic>Oxidizing agents</topic><topic>Predictions</topic><topic>QSAR</topic><topic>Quantitative Structure-Activity Relationship</topic><topic>Screening</topic><topic>Staining</topic><topic>Structure-activity relationships</topic><topic>Surgical implants</topic><topic>Therapeutic applications</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Das, Subrata</creatorcontrib><creatorcontrib>Laskar, Monjur A.</creatorcontrib><creatorcontrib>Sarker, Satyajit D.</creatorcontrib><creatorcontrib>Choudhury, Manabendra D.</creatorcontrib><creatorcontrib>Choudhury, Prakash Roy</creatorcontrib><creatorcontrib>Mitra, Abhijit</creatorcontrib><creatorcontrib>Jamil, Shajarahtunnur</creatorcontrib><creatorcontrib>Lathiff, Siti Mariam A.</creatorcontrib><creatorcontrib>Abdullah, Siti Awanis</creatorcontrib><creatorcontrib>Basar, Norazah</creatorcontrib><creatorcontrib>Nahar, Lutfun</creatorcontrib><creatorcontrib>Talukdar, Anupam D.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Chemoreception Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Phytochemical analysis</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Das, Subrata</au><au>Laskar, Monjur A.</au><au>Sarker, Satyajit D.</au><au>Choudhury, Manabendra D.</au><au>Choudhury, Prakash Roy</au><au>Mitra, Abhijit</au><au>Jamil, Shajarahtunnur</au><au>Lathiff, Siti Mariam A.</au><au>Abdullah, Siti Awanis</au><au>Basar, Norazah</au><au>Nahar, Lutfun</au><au>Talukdar, Anupam D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of Anti‐Alzheimer's Activity of Flavonoids Targeting Acetylcholinesterase in silico</atitle><jtitle>Phytochemical analysis</jtitle><addtitle>Phytochem Anal</addtitle><date>2017-07</date><risdate>2017</risdate><volume>28</volume><issue>4</issue><spage>324</spage><epage>331</epage><pages>324-331</pages><issn>0958-0344</issn><eissn>1099-1565</eissn><abstract>Introduction
Prenylated and pyrano‐flavonoids of the genus Artocarpus J. R. Forster & G. Forster are well known for their acetylcholinesterase (AChE) inhibitory, anti‐cholinergic, anti‐inflammatory, anti‐microbial, anti‐oxidant, anti‐proliferative and tyrosinase inhibitory activities. Some of these compounds have also been shown to be effective against Alzheimer's disease.
Objective
The aim of the in silico study was to establish protocols to predict the most effective flavonoid from prenylated and pyrano‐flavonoid classes for AChE inhibition linking to the potential treatment of Alzheimer's disease.
Methodology
Three flavonoids isolated from Artocarpus anisophyllus Miq. were selected for the study. With these compounds, Lipinski filter, ADME/Tox screening, molecular docking and quantitative structure–activity relationship (QSAR) were performed in silico. In vitro activity was evaluated by bioactivity staining based on the Ellman's method.
Results
In the Lipinski filter and ADME/Tox screening, all test compounds produced positive results, but in the target fishing, only one flavonoid could successfully target AChE. Molecular docking was performed on this flavonoid, and this compound gained the score as ˗13.5762. From the QSAR analysis the IC50 was found to be 1659.59 nM. Again, 100 derivatives were generated from the parent compound and docking was performed. The derivative compound 20 was the best scorer, i.e. ˗31.6392 and IC50 was predicted as 6.025 nM.
Conclusion
Results indicated that flavonoids could be efficient inhibitors of AChE and thus, could be useful in the management of Alzheimer's disease. Copyright © 2017 John Wiley & Sons, Ltd.
The authors established protocols to predict the anti‐Alzheimer's activity of flavonoids targeting acetylcholinesterase by the in silico approach. The key finding is that, the 5, 7 Dihydroxy‐4'‐methoxy‐8‐prenylflavanone a flavonoid could be an efficient inhibitor of AchE and thus, could be useful in the management of Alzheimer's disease.</abstract><cop>England</cop><pub>Wiley Subscription Services, Inc</pub><pmid>28168765</pmid><doi>10.1002/pca.2679</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0001-8916-2791</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Acetylcholinesterase ADME/Tox screening Alzheimer Disease - drug therapy Alzheimer's disease Antiinfectives and antibacterials Artocarpus anisophyllus Biochemistry Biocompatibility Biomedical materials Cholinesterase Inhibitors - pharmacology Derivatives Disease control Flavonoids Flavonoids - pharmacology Humans In vitro methods and tests Inflammation Inhibitors Lipinski filter Management Microorganisms Molecular docking Molecular Docking Simulation Molecular structure Neurodegenerative diseases Oxidizing agents Predictions QSAR Quantitative Structure-Activity Relationship Screening Staining Structure-activity relationships Surgical implants Therapeutic applications |
| title | Prediction of Anti‐Alzheimer's Activity of Flavonoids Targeting Acetylcholinesterase in silico |
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