Sentence examples for models for compounds from inspiring English sources

The computed 3-D conformer models for compounds in the 10-K and 156-K sets were downloaded from PubChem.

Figures 1, 2 and 3 showed the 3-dimensional docked models for compounds 18, 7 and 13 in the binding site of receptor protein DHFR.

The structures of the toxicokinetic models for compounds that are subject to biotransformation are shown in Figures 2– 4, and metabolites are labeled M1, M2, and M3.

The other two CA and IA are used as null models for compounds with similar or different modes of action, respectively.

In the initial stages of this project, the use of different fractions in the models for compounds with different Vss values or physicochemical parameters was considered.

We therefore initiated a 3D-QSAR study to develop predictive models for compound binding and identify structural features important for binding to this transporter.

Because PubChem3D does not compute conformer models for compound records with multiple covalent units [6, 18, 23], all of the resulting compounds in the Series B data sets are single-component compounds with computed 3-D conformer descriptions.

Consequently, the next two models for compounding relative risk are more plausible than the additive model.

Three different models for compounding relative risk were used for this purpose: The relative risk due to the different vectors were added together and then normalized on a scale of 0 to 1. Let Φu be the relative risk of cell k.

These are positive and encouraging findings, which are particularly promising for the feasibility of using these models for compound screening.

We therefore initiated molecular docking studies with the AutoDock and AutoDock Vina (ADVina) algorithms to develop predictive models for compound screening and to identify structural features important for binding to this transporter.