Binary Quantitative Structure-Activity Relationship Analysis to Increase the Predictive Ability of Structure-Based Virtual Screening Campaigns Targeting Cyclooxygenase-2
Structure-Based Virtual Screening (SBVS) campaigns employing Protein-Ligand Interaction Fingerprints (PLIF) identification have served as a powerful strategy in fragments and ligands identification, both retro- and prospectively. Most of the SBVS campaigns employed PLIF by comparing them to a refere...
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| Format: | Article |
| Language: | English |
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Department of Chemistry, Universitas Gadjah Mada
2017-07-01
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| Series: | Indonesian Journal of Chemistry |
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| Online Access: | https://jurnal.ugm.ac.id/ijc/article/view/24172 |
| _version_ | 1818568391073464320 |
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| author | Enade Perdana Istyastono |
| author_facet | Enade Perdana Istyastono |
| author_sort | Enade Perdana Istyastono |
| collection | DOAJ |
| description | Structure-Based Virtual Screening (SBVS) campaigns employing Protein-Ligand Interaction Fingerprints (PLIF) identification have served as a powerful strategy in fragments and ligands identification, both retro- and prospectively. Most of the SBVS campaigns employed PLIF by comparing them to a reference PLIF to calculate the Tanimoto-coefficient. Since the approach was reference dependent, it could lead to a very different discovery path if a different reference was used. In this article, references independent approach, i.e. decision trees construction using docking score and PLIF as the descriptors to increase the predictive ability of the SBVS campaigns in the identification of ligands for cyclooxygenase-2 is presented. The results showed that the binary Quantitative-Structure Activity Relationship (QSAR) analysis could significantly increase the predictive ability of the SBVS campaign. Moreover, the selected decision tree could also pinpoint the molecular determinants of the ligands binding to cyclooxygenase-2. |
| first_indexed | 2024-12-14T06:34:59Z |
| format | Article |
| id | doaj.art-77087bffe0d045bfafc254825e545b39 |
| institution | Directory Open Access Journal |
| issn | 1411-9420 2460-1578 |
| language | English |
| last_indexed | 2024-12-14T06:34:59Z |
| publishDate | 2017-07-01 |
| publisher | Department of Chemistry, Universitas Gadjah Mada |
| record_format | Article |
| series | Indonesian Journal of Chemistry |
| spelling | doaj.art-77087bffe0d045bfafc254825e545b392022-12-21T23:13:24ZengDepartment of Chemistry, Universitas Gadjah MadaIndonesian Journal of Chemistry1411-94202460-15782017-07-0117232232910.22146/ijc.2417217217Binary Quantitative Structure-Activity Relationship Analysis to Increase the Predictive Ability of Structure-Based Virtual Screening Campaigns Targeting Cyclooxygenase-2Enade Perdana Istyastono0Faculty of Pharmacy, Sanata Dharma UniversityStructure-Based Virtual Screening (SBVS) campaigns employing Protein-Ligand Interaction Fingerprints (PLIF) identification have served as a powerful strategy in fragments and ligands identification, both retro- and prospectively. Most of the SBVS campaigns employed PLIF by comparing them to a reference PLIF to calculate the Tanimoto-coefficient. Since the approach was reference dependent, it could lead to a very different discovery path if a different reference was used. In this article, references independent approach, i.e. decision trees construction using docking score and PLIF as the descriptors to increase the predictive ability of the SBVS campaigns in the identification of ligands for cyclooxygenase-2 is presented. The results showed that the binary Quantitative-Structure Activity Relationship (QSAR) analysis could significantly increase the predictive ability of the SBVS campaign. Moreover, the selected decision tree could also pinpoint the molecular determinants of the ligands binding to cyclooxygenase-2.https://jurnal.ugm.ac.id/ijc/article/view/24172Binary QSARdecision treeProtein-Ligand Interaction Fingerprints (PLIF)Structure-Based Virtual Screening (SBVS) |
| spellingShingle | Enade Perdana Istyastono Binary Quantitative Structure-Activity Relationship Analysis to Increase the Predictive Ability of Structure-Based Virtual Screening Campaigns Targeting Cyclooxygenase-2 Indonesian Journal of Chemistry Binary QSAR decision tree Protein-Ligand Interaction Fingerprints (PLIF) Structure-Based Virtual Screening (SBVS) |
| title | Binary Quantitative Structure-Activity Relationship Analysis to Increase the Predictive Ability of Structure-Based Virtual Screening Campaigns Targeting Cyclooxygenase-2 |
| title_full | Binary Quantitative Structure-Activity Relationship Analysis to Increase the Predictive Ability of Structure-Based Virtual Screening Campaigns Targeting Cyclooxygenase-2 |
| title_fullStr | Binary Quantitative Structure-Activity Relationship Analysis to Increase the Predictive Ability of Structure-Based Virtual Screening Campaigns Targeting Cyclooxygenase-2 |
| title_full_unstemmed | Binary Quantitative Structure-Activity Relationship Analysis to Increase the Predictive Ability of Structure-Based Virtual Screening Campaigns Targeting Cyclooxygenase-2 |
| title_short | Binary Quantitative Structure-Activity Relationship Analysis to Increase the Predictive Ability of Structure-Based Virtual Screening Campaigns Targeting Cyclooxygenase-2 |
| title_sort | binary quantitative structure activity relationship analysis to increase the predictive ability of structure based virtual screening campaigns targeting cyclooxygenase 2 |
| topic | Binary QSAR decision tree Protein-Ligand Interaction Fingerprints (PLIF) Structure-Based Virtual Screening (SBVS) |
| url | https://jurnal.ugm.ac.id/ijc/article/view/24172 |
| work_keys_str_mv | AT enadeperdanaistyastono binaryquantitativestructureactivityrelationshipanalysistoincreasethepredictiveabilityofstructurebasedvirtualscreeningcampaignstargetingcyclooxygenase2 |