QSAR studies on PIM1 and PIM2 inhibitors using statistical methods: a rustic strategy to screen for 5-(1H-indol-5-yl)-1,3,4-thiadiazol analogues and predict their PIM inhibitory activity

Abstract Background Quantitative structure activity relationship was carried out to study a series of PIM1 and PIM2 inhibitors. The present study was performed on twenty-five substituted 5-(1H-indol-5-yl)-1,3,4-thiadiazols as PIM1 and PIM2 inhibitors having pIC50 ranging from 5.55 to 9 µM and from 4...
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Gespeichert in:

Autor*in:	Adnane Aouidate [verfasserIn] Adib Ghaleb [verfasserIn] Mounir Ghamali [verfasserIn] Samir Chtita [verfasserIn] M’barek Choukrad [verfasserIn] Abdelouahid Sbai [verfasserIn] Mohammed Bouachrine [verfasserIn] Tahar Lakhlifi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	PIM1 PIM2 5-(1H-indol-5-yl)-1,3,4-thiadiazol-2-amines QSAR model

Übergeordnetes Werk:	In: Chemistry Central Journal - BMC, 2007, 11(2017), 1, Seite 10
Übergeordnetes Werk:	volume:11 ; year:2017 ; number:1 ; pages:10

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s13065-017-0269-1

Katalog-ID:	DOAJ05813607X

Internformat


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520			\|a Abstract Background Quantitative structure activity relationship was carried out to study a series of PIM1 and PIM2 inhibitors. The present study was performed on twenty-five substituted 5-(1H-indol-5-yl)-1,3,4-thiadiazols as PIM1 and PIM2 inhibitors having pIC50 ranging from 5.55 to 9 µM and from 4.66 to 8.22 µM, respectively, using genetic function algorithm for variable selection and multiple linear regression analysis (MLR) to establish unambiguous and simple QSAR models based on topological molecular descriptors. Results Results showed that the MLR predict activity in a satisfactory manner for both activities. Consequently, the aim of the current study is twofold, first, a simple linear QSAR model was developed, which could be easily handled by chemist to screen chemical databases, or design for new potent PIM1 and PIM2 inhibitors. Second, the outcomes extracted from the current study were exploited to predict the PIM inhibitory activity of some studied compound analogues. Conclusions The goal of this study is to develop easy and convenient QSAR model could be handled by everyone to screen chemical databases or to design newly PIM1 and PIM2 inhibitors derived from 5-(1H-indol-5-yl)-1,3,4-thiadiazol. Graphical abstract Flow chart of the methodology used in this work.
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allfields	10.1186/s13065-017-0269-1 doi (DE-627)DOAJ05813607X (DE-599)DOAJ47ae367aa30840b08d531fb8afe3bb06 DE-627 ger DE-627 rakwb eng QD1-999 Adnane Aouidate verfasserin aut QSAR studies on PIM1 and PIM2 inhibitors using statistical methods: a rustic strategy to screen for 5-(1H-indol-5-yl)-1,3,4-thiadiazol analogues and predict their PIM inhibitory activity 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Quantitative structure activity relationship was carried out to study a series of PIM1 and PIM2 inhibitors. The present study was performed on twenty-five substituted 5-(1H-indol-5-yl)-1,3,4-thiadiazols as PIM1 and PIM2 inhibitors having pIC50 ranging from 5.55 to 9 µM and from 4.66 to 8.22 µM, respectively, using genetic function algorithm for variable selection and multiple linear regression analysis (MLR) to establish unambiguous and simple QSAR models based on topological molecular descriptors. Results Results showed that the MLR predict activity in a satisfactory manner for both activities. Consequently, the aim of the current study is twofold, first, a simple linear QSAR model was developed, which could be easily handled by chemist to screen chemical databases, or design for new potent PIM1 and PIM2 inhibitors. Second, the outcomes extracted from the current study were exploited to predict the PIM inhibitory activity of some studied compound analogues. Conclusions The goal of this study is to develop easy and convenient QSAR model could be handled by everyone to screen chemical databases or to design newly PIM1 and PIM2 inhibitors derived from 5-(1H-indol-5-yl)-1,3,4-thiadiazol. Graphical abstract Flow chart of the methodology used in this work. PIM1 PIM2 5-(1H-indol-5-yl)-1,3,4-thiadiazol-2-amines QSAR model Chemistry Adib Ghaleb verfasserin aut Mounir Ghamali verfasserin aut Samir Chtita verfasserin aut M’barek Choukrad verfasserin aut Abdelouahid Sbai verfasserin aut Mohammed Bouachrine verfasserin aut Tahar Lakhlifi verfasserin aut In Chemistry Central Journal BMC, 2007 11(2017), 1, Seite 10 (DE-627)525475176 (DE-600)2272440-0 1752153X nnns volume:11 year:2017 number:1 pages:10 https://doi.org/10.1186/s13065-017-0269-1 kostenfrei https://doaj.org/article/47ae367aa30840b08d531fb8afe3bb06 kostenfrei http://link.springer.com/article/10.1186/s13065-017-0269-1 kostenfrei https://doaj.org/toc/1752-153X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2017 1 10
spelling	10.1186/s13065-017-0269-1 doi (DE-627)DOAJ05813607X (DE-599)DOAJ47ae367aa30840b08d531fb8afe3bb06 DE-627 ger DE-627 rakwb eng QD1-999 Adnane Aouidate verfasserin aut QSAR studies on PIM1 and PIM2 inhibitors using statistical methods: a rustic strategy to screen for 5-(1H-indol-5-yl)-1,3,4-thiadiazol analogues and predict their PIM inhibitory activity 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Quantitative structure activity relationship was carried out to study a series of PIM1 and PIM2 inhibitors. The present study was performed on twenty-five substituted 5-(1H-indol-5-yl)-1,3,4-thiadiazols as PIM1 and PIM2 inhibitors having pIC50 ranging from 5.55 to 9 µM and from 4.66 to 8.22 µM, respectively, using genetic function algorithm for variable selection and multiple linear regression analysis (MLR) to establish unambiguous and simple QSAR models based on topological molecular descriptors. Results Results showed that the MLR predict activity in a satisfactory manner for both activities. Consequently, the aim of the current study is twofold, first, a simple linear QSAR model was developed, which could be easily handled by chemist to screen chemical databases, or design for new potent PIM1 and PIM2 inhibitors. Second, the outcomes extracted from the current study were exploited to predict the PIM inhibitory activity of some studied compound analogues. Conclusions The goal of this study is to develop easy and convenient QSAR model could be handled by everyone to screen chemical databases or to design newly PIM1 and PIM2 inhibitors derived from 5-(1H-indol-5-yl)-1,3,4-thiadiazol. Graphical abstract Flow chart of the methodology used in this work. PIM1 PIM2 5-(1H-indol-5-yl)-1,3,4-thiadiazol-2-amines QSAR model Chemistry Adib Ghaleb verfasserin aut Mounir Ghamali verfasserin aut Samir Chtita verfasserin aut M’barek Choukrad verfasserin aut Abdelouahid Sbai verfasserin aut Mohammed Bouachrine verfasserin aut Tahar Lakhlifi verfasserin aut In Chemistry Central Journal BMC, 2007 11(2017), 1, Seite 10 (DE-627)525475176 (DE-600)2272440-0 1752153X nnns volume:11 year:2017 number:1 pages:10 https://doi.org/10.1186/s13065-017-0269-1 kostenfrei https://doaj.org/article/47ae367aa30840b08d531fb8afe3bb06 kostenfrei http://link.springer.com/article/10.1186/s13065-017-0269-1 kostenfrei https://doaj.org/toc/1752-153X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2017 1 10
allfields_unstemmed	10.1186/s13065-017-0269-1 doi (DE-627)DOAJ05813607X (DE-599)DOAJ47ae367aa30840b08d531fb8afe3bb06 DE-627 ger DE-627 rakwb eng QD1-999 Adnane Aouidate verfasserin aut QSAR studies on PIM1 and PIM2 inhibitors using statistical methods: a rustic strategy to screen for 5-(1H-indol-5-yl)-1,3,4-thiadiazol analogues and predict their PIM inhibitory activity 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Quantitative structure activity relationship was carried out to study a series of PIM1 and PIM2 inhibitors. The present study was performed on twenty-five substituted 5-(1H-indol-5-yl)-1,3,4-thiadiazols as PIM1 and PIM2 inhibitors having pIC50 ranging from 5.55 to 9 µM and from 4.66 to 8.22 µM, respectively, using genetic function algorithm for variable selection and multiple linear regression analysis (MLR) to establish unambiguous and simple QSAR models based on topological molecular descriptors. Results Results showed that the MLR predict activity in a satisfactory manner for both activities. Consequently, the aim of the current study is twofold, first, a simple linear QSAR model was developed, which could be easily handled by chemist to screen chemical databases, or design for new potent PIM1 and PIM2 inhibitors. Second, the outcomes extracted from the current study were exploited to predict the PIM inhibitory activity of some studied compound analogues. Conclusions The goal of this study is to develop easy and convenient QSAR model could be handled by everyone to screen chemical databases or to design newly PIM1 and PIM2 inhibitors derived from 5-(1H-indol-5-yl)-1,3,4-thiadiazol. Graphical abstract Flow chart of the methodology used in this work. PIM1 PIM2 5-(1H-indol-5-yl)-1,3,4-thiadiazol-2-amines QSAR model Chemistry Adib Ghaleb verfasserin aut Mounir Ghamali verfasserin aut Samir Chtita verfasserin aut M’barek Choukrad verfasserin aut Abdelouahid Sbai verfasserin aut Mohammed Bouachrine verfasserin aut Tahar Lakhlifi verfasserin aut In Chemistry Central Journal BMC, 2007 11(2017), 1, Seite 10 (DE-627)525475176 (DE-600)2272440-0 1752153X nnns volume:11 year:2017 number:1 pages:10 https://doi.org/10.1186/s13065-017-0269-1 kostenfrei https://doaj.org/article/47ae367aa30840b08d531fb8afe3bb06 kostenfrei http://link.springer.com/article/10.1186/s13065-017-0269-1 kostenfrei https://doaj.org/toc/1752-153X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2017 1 10
allfieldsGer	10.1186/s13065-017-0269-1 doi (DE-627)DOAJ05813607X (DE-599)DOAJ47ae367aa30840b08d531fb8afe3bb06 DE-627 ger DE-627 rakwb eng QD1-999 Adnane Aouidate verfasserin aut QSAR studies on PIM1 and PIM2 inhibitors using statistical methods: a rustic strategy to screen for 5-(1H-indol-5-yl)-1,3,4-thiadiazol analogues and predict their PIM inhibitory activity 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Quantitative structure activity relationship was carried out to study a series of PIM1 and PIM2 inhibitors. The present study was performed on twenty-five substituted 5-(1H-indol-5-yl)-1,3,4-thiadiazols as PIM1 and PIM2 inhibitors having pIC50 ranging from 5.55 to 9 µM and from 4.66 to 8.22 µM, respectively, using genetic function algorithm for variable selection and multiple linear regression analysis (MLR) to establish unambiguous and simple QSAR models based on topological molecular descriptors. Results Results showed that the MLR predict activity in a satisfactory manner for both activities. Consequently, the aim of the current study is twofold, first, a simple linear QSAR model was developed, which could be easily handled by chemist to screen chemical databases, or design for new potent PIM1 and PIM2 inhibitors. Second, the outcomes extracted from the current study were exploited to predict the PIM inhibitory activity of some studied compound analogues. Conclusions The goal of this study is to develop easy and convenient QSAR model could be handled by everyone to screen chemical databases or to design newly PIM1 and PIM2 inhibitors derived from 5-(1H-indol-5-yl)-1,3,4-thiadiazol. Graphical abstract Flow chart of the methodology used in this work. PIM1 PIM2 5-(1H-indol-5-yl)-1,3,4-thiadiazol-2-amines QSAR model Chemistry Adib Ghaleb verfasserin aut Mounir Ghamali verfasserin aut Samir Chtita verfasserin aut M’barek Choukrad verfasserin aut Abdelouahid Sbai verfasserin aut Mohammed Bouachrine verfasserin aut Tahar Lakhlifi verfasserin aut In Chemistry Central Journal BMC, 2007 11(2017), 1, Seite 10 (DE-627)525475176 (DE-600)2272440-0 1752153X nnns volume:11 year:2017 number:1 pages:10 https://doi.org/10.1186/s13065-017-0269-1 kostenfrei https://doaj.org/article/47ae367aa30840b08d531fb8afe3bb06 kostenfrei http://link.springer.com/article/10.1186/s13065-017-0269-1 kostenfrei https://doaj.org/toc/1752-153X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2017 1 10
allfieldsSound	10.1186/s13065-017-0269-1 doi (DE-627)DOAJ05813607X (DE-599)DOAJ47ae367aa30840b08d531fb8afe3bb06 DE-627 ger DE-627 rakwb eng QD1-999 Adnane Aouidate verfasserin aut QSAR studies on PIM1 and PIM2 inhibitors using statistical methods: a rustic strategy to screen for 5-(1H-indol-5-yl)-1,3,4-thiadiazol analogues and predict their PIM inhibitory activity 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Quantitative structure activity relationship was carried out to study a series of PIM1 and PIM2 inhibitors. The present study was performed on twenty-five substituted 5-(1H-indol-5-yl)-1,3,4-thiadiazols as PIM1 and PIM2 inhibitors having pIC50 ranging from 5.55 to 9 µM and from 4.66 to 8.22 µM, respectively, using genetic function algorithm for variable selection and multiple linear regression analysis (MLR) to establish unambiguous and simple QSAR models based on topological molecular descriptors. Results Results showed that the MLR predict activity in a satisfactory manner for both activities. Consequently, the aim of the current study is twofold, first, a simple linear QSAR model was developed, which could be easily handled by chemist to screen chemical databases, or design for new potent PIM1 and PIM2 inhibitors. Second, the outcomes extracted from the current study were exploited to predict the PIM inhibitory activity of some studied compound analogues. Conclusions The goal of this study is to develop easy and convenient QSAR model could be handled by everyone to screen chemical databases or to design newly PIM1 and PIM2 inhibitors derived from 5-(1H-indol-5-yl)-1,3,4-thiadiazol. Graphical abstract Flow chart of the methodology used in this work. PIM1 PIM2 5-(1H-indol-5-yl)-1,3,4-thiadiazol-2-amines QSAR model Chemistry Adib Ghaleb verfasserin aut Mounir Ghamali verfasserin aut Samir Chtita verfasserin aut M’barek Choukrad verfasserin aut Abdelouahid Sbai verfasserin aut Mohammed Bouachrine verfasserin aut Tahar Lakhlifi verfasserin aut In Chemistry Central Journal BMC, 2007 11(2017), 1, Seite 10 (DE-627)525475176 (DE-600)2272440-0 1752153X nnns volume:11 year:2017 number:1 pages:10 https://doi.org/10.1186/s13065-017-0269-1 kostenfrei https://doaj.org/article/47ae367aa30840b08d531fb8afe3bb06 kostenfrei http://link.springer.com/article/10.1186/s13065-017-0269-1 kostenfrei https://doaj.org/toc/1752-153X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2017 1 10
language	English
source	In Chemistry Central Journal 11(2017), 1, Seite 10 volume:11 year:2017 number:1 pages:10
sourceStr	In Chemistry Central Journal 11(2017), 1, Seite 10 volume:11 year:2017 number:1 pages:10
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institution	findex.gbv.de
topic_facet	PIM1 PIM2 5-(1H-indol-5-yl)-1,3,4-thiadiazol-2-amines QSAR model Chemistry
isfreeaccess_bool	true
container_title	Chemistry Central Journal
authorswithroles_txt_mv	Adnane Aouidate @@aut@@ Adib Ghaleb @@aut@@ Mounir Ghamali @@aut@@ Samir Chtita @@aut@@ M’barek Choukrad @@aut@@ Abdelouahid Sbai @@aut@@ Mohammed Bouachrine @@aut@@ Tahar Lakhlifi @@aut@@
publishDateDaySort_date	2017-01-01T00:00:00Z
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callnumber-first	Q - Science
author	Adnane Aouidate
spellingShingle	Adnane Aouidate misc QD1-999 misc PIM1 misc PIM2 misc 5-(1H-indol-5-yl)-1,3,4-thiadiazol-2-amines misc QSAR model misc Chemistry QSAR studies on PIM1 and PIM2 inhibitors using statistical methods: a rustic strategy to screen for 5-(1H-indol-5-yl)-1,3,4-thiadiazol analogues and predict their PIM inhibitory activity
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topic_title	QD1-999 QSAR studies on PIM1 and PIM2 inhibitors using statistical methods: a rustic strategy to screen for 5-(1H-indol-5-yl)-1,3,4-thiadiazol analogues and predict their PIM inhibitory activity PIM1 PIM2 5-(1H-indol-5-yl)-1,3,4-thiadiazol-2-amines QSAR model
topic	misc QD1-999 misc PIM1 misc PIM2 misc 5-(1H-indol-5-yl)-1,3,4-thiadiazol-2-amines misc QSAR model misc Chemistry
topic_unstemmed	misc QD1-999 misc PIM1 misc PIM2 misc 5-(1H-indol-5-yl)-1,3,4-thiadiazol-2-amines misc QSAR model misc Chemistry
topic_browse	misc QD1-999 misc PIM1 misc PIM2 misc 5-(1H-indol-5-yl)-1,3,4-thiadiazol-2-amines misc QSAR model misc Chemistry
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title	QSAR studies on PIM1 and PIM2 inhibitors using statistical methods: a rustic strategy to screen for 5-(1H-indol-5-yl)-1,3,4-thiadiazol analogues and predict their PIM inhibitory activity
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title_full	QSAR studies on PIM1 and PIM2 inhibitors using statistical methods: a rustic strategy to screen for 5-(1H-indol-5-yl)-1,3,4-thiadiazol analogues and predict their PIM inhibitory activity
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author_browse	Adnane Aouidate Adib Ghaleb Mounir Ghamali Samir Chtita M’barek Choukrad Abdelouahid Sbai Mohammed Bouachrine Tahar Lakhlifi
container_volume	11
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title_sort	qsar studies on pim1 and pim2 inhibitors using statistical methods: a rustic strategy to screen for 5-(1h-indol-5-yl)-1,3,4-thiadiazol analogues and predict their pim inhibitory activity
callnumber	QD1-999
title_auth	QSAR studies on PIM1 and PIM2 inhibitors using statistical methods: a rustic strategy to screen for 5-(1H-indol-5-yl)-1,3,4-thiadiazol analogues and predict their PIM inhibitory activity
abstract	Abstract Background Quantitative structure activity relationship was carried out to study a series of PIM1 and PIM2 inhibitors. The present study was performed on twenty-five substituted 5-(1H-indol-5-yl)-1,3,4-thiadiazols as PIM1 and PIM2 inhibitors having pIC50 ranging from 5.55 to 9 µM and from 4.66 to 8.22 µM, respectively, using genetic function algorithm for variable selection and multiple linear regression analysis (MLR) to establish unambiguous and simple QSAR models based on topological molecular descriptors. Results Results showed that the MLR predict activity in a satisfactory manner for both activities. Consequently, the aim of the current study is twofold, first, a simple linear QSAR model was developed, which could be easily handled by chemist to screen chemical databases, or design for new potent PIM1 and PIM2 inhibitors. Second, the outcomes extracted from the current study were exploited to predict the PIM inhibitory activity of some studied compound analogues. Conclusions The goal of this study is to develop easy and convenient QSAR model could be handled by everyone to screen chemical databases or to design newly PIM1 and PIM2 inhibitors derived from 5-(1H-indol-5-yl)-1,3,4-thiadiazol. Graphical abstract Flow chart of the methodology used in this work.
abstractGer	Abstract Background Quantitative structure activity relationship was carried out to study a series of PIM1 and PIM2 inhibitors. The present study was performed on twenty-five substituted 5-(1H-indol-5-yl)-1,3,4-thiadiazols as PIM1 and PIM2 inhibitors having pIC50 ranging from 5.55 to 9 µM and from 4.66 to 8.22 µM, respectively, using genetic function algorithm for variable selection and multiple linear regression analysis (MLR) to establish unambiguous and simple QSAR models based on topological molecular descriptors. Results Results showed that the MLR predict activity in a satisfactory manner for both activities. Consequently, the aim of the current study is twofold, first, a simple linear QSAR model was developed, which could be easily handled by chemist to screen chemical databases, or design for new potent PIM1 and PIM2 inhibitors. Second, the outcomes extracted from the current study were exploited to predict the PIM inhibitory activity of some studied compound analogues. Conclusions The goal of this study is to develop easy and convenient QSAR model could be handled by everyone to screen chemical databases or to design newly PIM1 and PIM2 inhibitors derived from 5-(1H-indol-5-yl)-1,3,4-thiadiazol. Graphical abstract Flow chart of the methodology used in this work.
abstract_unstemmed	Abstract Background Quantitative structure activity relationship was carried out to study a series of PIM1 and PIM2 inhibitors. The present study was performed on twenty-five substituted 5-(1H-indol-5-yl)-1,3,4-thiadiazols as PIM1 and PIM2 inhibitors having pIC50 ranging from 5.55 to 9 µM and from 4.66 to 8.22 µM, respectively, using genetic function algorithm for variable selection and multiple linear regression analysis (MLR) to establish unambiguous and simple QSAR models based on topological molecular descriptors. Results Results showed that the MLR predict activity in a satisfactory manner for both activities. Consequently, the aim of the current study is twofold, first, a simple linear QSAR model was developed, which could be easily handled by chemist to screen chemical databases, or design for new potent PIM1 and PIM2 inhibitors. Second, the outcomes extracted from the current study were exploited to predict the PIM inhibitory activity of some studied compound analogues. Conclusions The goal of this study is to develop easy and convenient QSAR model could be handled by everyone to screen chemical databases or to design newly PIM1 and PIM2 inhibitors derived from 5-(1H-indol-5-yl)-1,3,4-thiadiazol. Graphical abstract Flow chart of the methodology used in this work.
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container_issue	1
title_short	QSAR studies on PIM1 and PIM2 inhibitors using statistical methods: a rustic strategy to screen for 5-(1H-indol-5-yl)-1,3,4-thiadiazol analogues and predict their PIM inhibitory activity
url	https://doi.org/10.1186/s13065-017-0269-1 https://doaj.org/article/47ae367aa30840b08d531fb8afe3bb06 http://link.springer.com/article/10.1186/s13065-017-0269-1 https://doaj.org/toc/1752-153X
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author2	Adib Ghaleb Mounir Ghamali Samir Chtita M’barek Choukrad Abdelouahid Sbai Mohammed Bouachrine Tahar Lakhlifi
author2Str	Adib Ghaleb Mounir Ghamali Samir Chtita M’barek Choukrad Abdelouahid Sbai Mohammed Bouachrine Tahar Lakhlifi
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up_date	2024-07-03T16:11:54.798Z
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QSAR studies on PIM1 and PIM2 inhibitors using statistical methods: a rustic strategy to screen for 5-(1H-indol-5-yl)-1,3,4-thiadiazol analogues and predict their PIM inhibitory activity

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