ライフサイエンスコーパス: QSPR

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 s, artificial neural networks were used in a QSPR study related to the gelling properties measured by

4 Molecular docking analyses and QSPR of pregabalin confirmed its suitability as a new IO

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

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

7 structures with the purpose of establishing QSPR models.

8 cess to microscopic properties by exploiting QSPR.

9 Additionally, this study provides the first QSPR analysis focused on NAPL-water interfacial adsorpti

10 descriptors can serve as effective tools for QSPR modeling and drug development, offering a bridge be

11 and in tier II, it inherits the fundamental QSPR knowledge from previous steps through a dynamic int

12 A general QSPR model including mainly topological descriptors was

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

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

15 ining set were predicted from the integrated QSPR models.

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

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

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

19 Predicted QSPR results were in good agreement with experimental am

20 ructure-activity/property relationship (QSAR/QSPR) modelling.

21 ructure-activity/property relationship (QSAR/QSPR) models, provides exciting opportunities to optimiz

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

23 uantitative structure-property relationship (QSPR) method.

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

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

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

27 uantitative structure-property relationship (QSPR) modeling predicts the physical, synthetic, and nat

28 uantitative structure-property relationship (QSPR) modeling to support anti-tuberculosis drug discove

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

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

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

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

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

34 uantitative structure-property relationship (QSPR) studies rely on molecular descriptors that link ch

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

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

37 uantitative structure-property relationship (QSPR), including 216 contaminants of emerging concern (C

38 uantitative structure-property relationship (QSPR)-based model was also built to predict the APCI ion

39 antitative structure-property relationships (QSPR) using MDs to predict solute-dependent parameters i

40 antitative structure-property relationships (QSPR), which led to a further correction of the matrix e

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

42 dducts was also important to transfer the RF QSPR model reliably.

43 are sequence-dependent; therefore, a second QSPR model for the prediction of the transferability fac

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

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

46 The QSPR results narrow the list to 966 elemental compositio

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