ライフサイエンスコーパス: QSPR

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1 QSPR estimates verified 4,4',4''-TCPM and 4,4'4,''-TCPMO

2 nt agreement with the experiments using a 3D-QSPR approach.

3 t in concert with new molecular descriptors, QSPR classification models were generated to provide imp

4 The developed QSPR approach showed to be of practical value for distin

5 structures with the purpose of establishing QSPR models.

6 A general QSPR model including mainly topological descriptors was

7 ading efficiency that were not used by us in QSPR model development were identified in the published

8 , thus enabling the generation of integrated QSPR models, which allow the prediction of chromatograph

9 ining set were predicted from the integrated QSPR models.

10 The predictions from these integrated QSPR models in general showed good agreement with the ex

11 presents the first successful application of QSPR models for the computer-model-driven design of lipo

12 ed structure, we herewith propose the use of QSPR as a support tool for quality control of screening

13 Predicted QSPR results were in good agreement with experimental am

14 uantitative structure property relationship (QSPR) based modeling approaches.

15 uantitative structure-property relationship (QSPR) method.

16 uantitative structure-property relationship (QSPR) model for the prediction of aqueous solubility (at

17 uantitative structure-property relationship (QSPR) model to find the best physicochemical descriptors

18 uantitative structure-property relationship (QSPR) modeling in order to annotate the CN.

19 uantitative structure-property relationship (QSPR) models implemented in CASI predict three specific

20 uantitative structure-property relationship (QSPR) models of metabolic turnover rate for compounds in

21 uantitative Structure Property Relationship (QSPR) models that correlate drugs' structural, physical

22 uantitative structure-property relationship (QSPR) models that employed a support vector machine regr

23 uantitative Structure-Property Relationship (QSPR) models, using two independent approaches RapidMine

24 uantitative structure-property relationship (QSPR) to support design.

25 uantitative structure property relationship (QSPR) was developed to describe the effects of the penet

26 rectly classified by our previously reported QSPR models developed with Iterative Stochastic Eliminat

27 The training set QSPR models were characterized by high internal accuracy

28 In addition, nonlinear SVM QSPR prediction models were generated and employed as a

29 o three predicted parameter matches from the QSPR models to generate a combined CASI Score representi

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