4D-QSAR and MIA-QSAR study on the Bruton's tyrosine kinase (Btk) inhibitors

In this paper, 4D-QSAR analysis base on LQTA-QSAR method and MIA-QSAR analysis were presented. And a combination model of 4D-QSAR model and MIA-QSAR model with better predictive performance was built. The 4D-QSAR descriptors were obtained by calculating the interaction energies between the probes an...

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Published inJournal of molecular graphics & modelling Vol. 92; pp. 357 - 362
Main Authors Ma, Wenzhi, Wang, Yanyu, Chu, Dongchen, Yan, Hong
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 01.11.2019
Subjects
4D-QSAR
Btk inhibitors
Combination model
Design and model programs
Combination model
Btk inhibitors
4D-QSAR
Design and model programs
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Abstract In this paper, 4D-QSAR analysis base on LQTA-QSAR method and MIA-QSAR analysis were presented. And a combination model of 4D-QSAR model and MIA-QSAR model with better predictive performance was built. The 4D-QSAR descriptors were obtained by calculating the interaction energies between the probes and aligned conformational ensemble profiles (CEP) resulting from molecular dynamics simulation. The MIA descriptors were generated from aligned images of chemical structure. Those descriptors were filtered and employed in partial least squares (PLS) regression. The combination model was built by the merging the descriptors generated by the two methods of 4D-QSAR and MIA-QSAR analysis. Those models were built using programs written by authors, and anyone can download those programs at https://github.com/masgils. [Display omitted] •4D-QSAR and MIA-QSAR analysis on Btk inhibitors.•Modeling programs written by authors can be downloaded for free.•A feasible way of combining descriptors was given in the article.
AbstractList In this paper, 4D-QSAR analysis base on LQTA-QSAR method and MIA-QSAR analysis were presented. And a combination model of 4D-QSAR model and MIA-QSAR model with better predictive performance was built. The 4D-QSAR descriptors were obtained by calculating the interaction energies between the probes and aligned conformational ensemble profiles (CEP) resulting from molecular dynamics simulation. The MIA descriptors were generated from aligned images of chemical structure. Those descriptors were filtered and employed in partial least squares (PLS) regression. The combination model was built by the merging the descriptors generated by the two methods of 4D-QSAR and MIA-QSAR analysis. Those models were built using programs written by authors, and anyone can download those programs at https://github.com/masgils.
In this paper, 4D-QSAR analysis base on LQTA-QSAR method and MIA-QSAR analysis were presented. And a combination model of 4D-QSAR model and MIA-QSAR model with better predictive performance was built. The 4D-QSAR descriptors were obtained by calculating the interaction energies between the probes and aligned conformational ensemble profiles (CEP) resulting from molecular dynamics simulation. The MIA descriptors were generated from aligned images of chemical structure. Those descriptors were filtered and employed in partial least squares (PLS) regression. The combination model was built by the merging the descriptors generated by the two methods of 4D-QSAR and MIA-QSAR analysis. Those models were built using programs written by authors, and anyone can download those programs at https://github.com/masgils. [Display omitted] •4D-QSAR and MIA-QSAR analysis on Btk inhibitors.•Modeling programs written by authors can be downloaded for free.•A feasible way of combining descriptors was given in the article.
Author Ma, Wenzhi
Wang, Yanyu
Chu, Dongchen
Yan, Hong
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Keywords Combination model
Btk inhibitors
4D-QSAR
Design and model programs
Language English
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Snippet In this paper, 4D-QSAR analysis base on LQTA-QSAR method and MIA-QSAR analysis were presented. And a combination model of 4D-QSAR model and MIA-QSAR model with...
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SubjectTerms 4D-QSAR
Btk inhibitors
Combination model
Design and model programs
Title 4D-QSAR and MIA-QSAR study on the Bruton's tyrosine kinase (Btk) inhibitors
URI https://dx.doi.org/10.1016/j.jmgm.2019.08.009
https://www.ncbi.nlm.nih.gov/pubmed/31450065
https://search.proquest.com/docview/2281102210
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