Development of decision trees to discriminate HDAC8 inhibitors and non-inhibitors using recursive partitioning
Histone deacetylase 8 (HDAC8) is involved in malignancy. Overexpression of HDAC8 is correlated with various cancers. Design of selective HDAC8 inhibitors is always a challenging task to the chemistry audiences. In this communication, a diverse set comprising large number of compounds are subjected to recursive partitioning (RP) analysis to develop decision trees to discriminate compounds into HDAC8 inhibitors (active) and non-inhibitors (inactive). Acquiring knowledge about the essential structural and physicochemical parameters can be useful in designing potential and selective HDAC8 inhibitors. Moreover, this work validates our previous results observed in Bayesian modelling study of this dataset. This comparative learning will surely enrich drug discovery aspects related to HDAC8 inhibitors.Communicated by Ramaswamy H. Sarma.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:39 |
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| Enthalten in: |
Journal of biomolecular structure & dynamics - 39(2021), 1 vom: 15. Jan., Seite 1-8 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Amin, Sk Abdul [VerfasserIn] |
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| Links: |
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| Themen: |
Cancer |
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| Anmerkungen: |
Date Completed 18.06.2021 Date Revised 18.06.2021 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1080/07391102.2019.1661876 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Histone deacetylase 8 (HDAC8) is involved in malignancy. Overexpression of HDAC8 is correlated with various cancers. Design of selective HDAC8 inhibitors is always a challenging task to the chemistry audiences. In this communication, a diverse set comprising large number of compounds are subjected to recursive partitioning (RP) analysis to develop decision trees to discriminate compounds into HDAC8 inhibitors (active) and non-inhibitors (inactive). Acquiring knowledge about the essential structural and physicochemical parameters can be useful in designing potential and selective HDAC8 inhibitors. Moreover, this work validates our previous results observed in Bayesian modelling study of this dataset. This comparative learning will surely enrich drug discovery aspects related to HDAC8 inhibitors.Communicated by Ramaswamy H. Sarma | ||
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