Quantitative Structure–Activity Relationships Study on the Ah Receptor Binding Affinities of Polybrominated Diphenyl Ethers Using a Support Vector Machine

2007 ◽  
Vol 26 (4) ◽  
pp. 536-541 ◽  
Author(s):  
Gang Zheng ◽  
Man Xiao ◽  
Xiaohua Lu
Keyword(s):  
Support Vector Machine ◽  
Receptor Binding ◽  
Polybrominated Diphenyl Ethers ◽  
Ah Receptor ◽  
Support Vector ◽  
Binding Affinities ◽  
Quantitative Structure ◽  
Structure Activity ◽  
Diphenyl Ethers ◽  
Quantitative Structure Activity Relationships

QSAR Modeling and Prediction of Triptan Binding Affinities

2021 ◽  
Vol 6 (2) ◽  
pp. 19-28
Author(s):  
Lucas Alland ◽  
Solomon H. Jacobson
Keyword(s):  
Neural Network ◽  
Binding Affinities ◽  
Potential Drug ◽  
Quantitative Structure ◽  
Qsar Modeling ◽  
Structure Activity Relationships ◽  
Drug Candidates ◽  
Modeling And Prediction ◽  
Structure Activity ◽  
Quantitative Structure Activity Relationships

The purpose of this study was to use quantitative structure-activity relationships (QSARs) to identify new triptan molecules that selectively bind to h 5-HT1B and h 5-HT1D to a greater extent than to the similar h 5-HT1A receptor in order to identify novel compounds that could lead to an alternative and potentially superior migraine relief drug. Due to its possibility in migraine abortive properties, binding affinities to h 5-HT1F were also modeled. Binding affinities for 12 different triptan drugs and structurally similar substances were compiled from the literature, and descriptors were generated for those and other potential drug candidates using a variety of programs. The most significant descriptors were identified using a stepwise model, and the final QSARs were built for each activity with those descriptors, and a neural network. QSARs for all four activities were validated using a holdback method and were all found to be highly accurate. With these QSARs, activities of novel compounds similar to triptan drugs were predicted and three potential drug candidates were suggested.

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