Optimization of biaryl piperidine and 4-amino-2-biarylurea MCH1 receptor antagonists using QSAR modeling, classification techniques and virtual screening

@article{Melagraki2007OptimizationOB,
  title={Optimization of biaryl piperidine and 4-amino-2-biarylurea MCH1 receptor antagonists using QSAR modeling, classification techniques and virtual screening},
  author={Georgia Melagraki and Antreas Afantitis and Haralambos Sarimveis and P. Koutentis and John Markopoulos and Olga Igglessi-Markopoulou},
  journal={Journal of Computer-Aided Molecular Design},
  year={2007},
  volume={21},
  pages={251-267}
}
This paper presents the results of an optimization study on biaryl piperidine and 4-amino-2-biarylurea MCH1 receptor antagonists, which was accomplished by using quantitative-structure activity relationships (QSARs), classification and virtual screening techniques. First, a linear QSAR model was developed using Multiple Linear Regression (MLR) Analysis, while the Elimination Selection-Stepwise Regression (ES-SWR) method was adopted for selecting the most suitable input variables. The predictive… 
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References

SHOWING 1-10 OF 35 REFERENCES
Investigation of substituent effect of 1-(3,3-diphenylpropyl)-piperidinyl phenylacetamides on CCR5 binding affinity using QSAR and virtual screening techniques
TLDR
A linear quantitative–structure activity relationship model is developed in this work using Multiple Linear Regression Analysis as applied to a series of 51 1-(3,3-diphenylpropyl)-piperidinyl phenylacetamides derivatives with CCR5 binding affinity, leading to a number of guanidine derivatives with significantly improved predicted activities.
Application of predictive QSAR models to database mining: identification and experimental validation of novel anticonvulsant compounds.
We have developed a drug discovery strategy that employs variable selection quantitative structure-activity relationship (QSAR) models for chemical database mining. The approach starts with the
A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis
SummaryA quantitative–structure activity relationship was obtained by applying Multiple Linear Regression Analysis to a series of 80 1-[2-hydroxyethoxy-methyl]-6-(phenylthio) thymine (HEPT)
Identification of 4-amino-2-cyclohexylaminoquinazolines as metabolically stable melanin-concentrating hormone receptor 1 antagonists.
TLDR
The optimization of the distance between two key pharmacophore features within first hit compounds 1a and 2a led to the identification of a new class of potent non-peptidic antagonists for the MCH-R1 based around 4-amino-2-cyclohexylaminoquinazolines, which showed good metabolic stability in liver microsomes from human and rat.
Discovery and SAR of 4-amino-2-biarylbutylurea MCH 1 receptor antagonists through solid-phase parallel synthesis.
TLDR
Lead optimization efforts using solid-phase parallel synthesis resulted in the defined structure-activity relationships and the identification of 4-amino-2-biarylbutylureas, such as 11g, as potent single digit nanomolar MCH1R antagonists.
Synthesis and structure-activity relationships of biarylcarboxamide bis-aminopyrrolidine urea derived small-molecule antagonists of the melanin-concentrating hormone receptor-1 (MCH-R1).
A novel series of bis-aminopyrrolidine ureas containing either a 4-biphenylcarboxmide or 5-phenyl-2-thiophenecarboxamide group have been identified as potent and functional antagonists of the
Comparison of Ranking Methods for Virtual Screening in Lead-Discovery Programs
This paper discusses the use of several rank-based virtual screening methods for prioritizing compounds in lead-discovery programs, given a training set for which both structural and bioactivity data
Synthesis and evaluation of 2-amino-8-alkoxy quinolines as MCHr1 antagonists. Part 2.
TLDR
The continued SAR investigation of 2-amino-8-alkoxy quinolines as melanin concentrating hormone receptor-1 (MCHr1) antagonists is reported, with the identification of compounds such as 33, 34 and 37, which demonstrate single digit nanomolar antagonism of MCHr 1-mediated Ca(2+) release.
Lead optimization of 4-(dimethylamino)quinazolines, potent and selective antagonists for the melanin-concentrating hormone receptor 1.
TLDR
The combination of the elaboration of both the linker portion and the terminal phenyl ring provided N-(cis-4-{[4-(dimethylamino)quinazolin-2-yl]amino}cyclohexyl)-3,4-difluorobenzamide hydrochloride 28 (ATC0175), which showed excellent antagonist activity at the MCH-R1 and good selectivity over the Y5 and the alpha2A receptors.
Virtual Screening of Molecular Databases Using a Support Vector Machine
TLDR
The SVM algorithm is applied to the problem of virtual screening for molecules with a desired activity by using a modified version of the standard SVM function to rank molecules and employing a simple and novel criterion for picking molecular descriptors.
...
1
2
3
4
...