Big Chemical Encyclopedia

Chemical substances, components, reactions, process design ...

Articles Figures Tables About

Multiplicity structured testing

When compounds are selected according to SMD, this necessitates the adequate description of their structures by means of quantitative variables, "structure descriptors". This description can then be used after the compound selection, synthesis, and biological testing to formulate quantitative models between structural variation and activity variation, so called Quantitative Structure Activity Relationships (QSARs). For extensive reviews, see references 3 and 4. With multiple structure descriptors and multiple biological activity variables (responses), these models are necessarily multivariate (M-QSAR) in their nature, making the Partial Least Squares Projections to Latent Structures (PLS) approach suitable for the data analysis. PLS is a statistical method, which relates a multivariate descriptor data set (X) to a multivariate response data set Y. PLS is well described elsewhere and will not be described any further here [42, 43]. [Pg.214]

EN 15416-2 2008, Adhesives for load bearing timber structures — Test methods — Part 2 Static load test of multiple bondfine specimens in compression shear. [Pg.462]

Aqueous solubility is selected to demonstrate the E-state application in QSPR studies. Huuskonen et al. modeled the aqueous solubihty of 734 diverse organic compounds with multiple linear regression (MLR) and artificial neural network (ANN) approaches [27]. The set of structural descriptors comprised 31 E-state atomic indices, and three indicator variables for pyridine, ahphatic hydrocarbons and aromatic hydrocarbons, respectively. The dataset of734 chemicals was divided into a training set ( =675), a vahdation set (n=38) and a test set (n=21). A comparison of the MLR results (training, r =0.94, s=0.58 vahdation r =0.84, s=0.67 test, r =0.80, s=0.87) and the ANN results (training, r =0.96, s=0.51 vahdation r =0.85, s=0.62 tesL r =0.84, s=0.75) indicates a smah improvement for the neural network model with five hidden neurons. These QSPR models may be used for a fast and rehable computahon of the aqueous solubihty for diverse orgarhc compounds. [Pg.93]


See other pages where Multiplicity structured testing is mentioned: [Pg.325]    [Pg.90]    [Pg.56]    [Pg.250]    [Pg.323]    [Pg.129]    [Pg.256]    [Pg.287]    [Pg.14]    [Pg.157]    [Pg.14]    [Pg.164]    [Pg.48]    [Pg.188]    [Pg.153]    [Pg.132]    [Pg.2869]    [Pg.325]    [Pg.250]    [Pg.737]    [Pg.1752]    [Pg.1770]    [Pg.497]    [Pg.537]    [Pg.86]    [Pg.445]    [Pg.19]    [Pg.405]    [Pg.79]    [Pg.1167]    [Pg.152]    [Pg.163]    [Pg.279]    [Pg.42]    [Pg.107]    [Pg.586]    [Pg.104]    [Pg.335]    [Pg.71]    [Pg.397]    [Pg.38]    [Pg.47]    [Pg.49]    [Pg.52]    [Pg.190]    [Pg.144]    [Pg.218]   
See also in sourсe #XX -- [ Pg.7 , Pg.8 , Pg.15 ]




SEARCH



Multiple testing

Multiple testing multiplicity

Structured testing, with multiplicity

Test structures

© 2024 chempedia.info