Descriptor GRIND

Afzelius, L., Masimieembwa, C.M., Karlen, A., Andeesson, T.B., and Zamora, 1. Discriminant and quantitative PLS analysis of competitive CYP2C9 inhibitors versus non-inhibitors using alignment independent GRIND descriptors. 

Another modification of the VolSurf approach to analyze molecular interaction fields was described by Pastor et al. (2000). The same information previously been used to compute VolSurf descriptors now served to compute GRIND descriptors. In contrast to VolSurf, these descriptors capture information about pharmacophoric groups and their distances within individual molecules. These descriptors represent favorable interaction energy regions where groups... 

Figure 6.7. Two potential situations leading to ambiguous GRIND descriptors. See text for details.

The GRIND descriptors are insensitive to the chirality of the structures. This has the undesirable side effect of providing exactly the same description for the two enantiomers associated with any chiral center. Diastereomers might, on the contrary, produce different correlograms, due to the presence of differences in the internal geometry. 

Einally, there is a fourth program which encapsulates GRIND descriptors named ALMOND. ALMOND theory is described in Chapter 6. What we will describe below refers to our involvement in utilizing the GRIND descriptors to model and understand various ADME and safety properties. 

Secondly, we will describe the use of GRIND descriptors to obtain a general model for inhibition of the K channel in the heart, known as the Human Ether-a-go-go Gene (HERG). 

Figure 9.1. PCA score plot derived from the analysis of the GRIND descriptors calculated for the entire training set of 129 compounds. The gray objects represent the compounds that form the training set and the black dots represent the molecules of the test set.

Correlations between the HERG chaimel inhibition and GRIND descriptors were analyzed using multivariate techniques such as principal component analysis (PCA) and PLS regression analysis. 

The PLS multivariate data analysis of the training set was carried out on the descriptors matrix to correlate the complete set of variables with the activity data. From a total of 710 variables, 559 active variables remained after filtering descriptors with no variability by the ALMOND program. The PLS analysis resulted in four latent variables (LVs) with / = 0.76. The cross validation of the model using the leave-one-out (LOO) method yielded values of 0.72. As shown in Table 9.2, the GRIND descriptors 11-36, 44-49, 12-28, 13-42, 14-46, 24-46 and 34-45 were found to correlate with the inhibition activity in terms of high coefficients. 

I 9 Molecular Interaction Fields in ADME and Safety Table9.2 Salient GRIND descriptors in PLS models. 

GRIND descriptor GRID MIFs Subset 1 Subset II ... 

PLS analysis was performed on the reduced set to identify a correlation between the complete set of variables and the activity data. The ALMOND program kept 624 active variables out of a total of 750 variables after filtering out descriptors with no variability. The 45 compounds with two basic nitrogen atoms were proton-ated on just one of the two basic centers. For these compounds, two models were built using one of the two isomers at a time. The isomer that produced the model with the best r was chosen. The PLS analysis resulted in a model with three LVs and r = 0.77. The cross validation of the model by the LOO method yielded q values of 0.74. As shown in Table 9.2, the GRIND descriptors that had high coefficients in the PLS model were 11-36, 44-58, 12-41, 13-40, 14-47, 24-52, and 34-49. 

Our studies also suggested that H-bond acceptors play an important role for compounds that bind the HERG channel (GRIND descriptors 13 and 34 in both the pharmacophoric models, as shown in Fig. 9.8). The statistical relevance of the MIFs generated by the hydrophobic probe confirmed the assumptions that were made in a previous CoMSiA model [17] regarding the presence of a hydrophobic feature. 

Figure 9.8. The GRIND descriptors from field generated by a H-bond donor group and hydrophobic area in moll 1 (top) and mol316172 (bottom).

Figure 9.12. Projection of the GRIND descriptors identified by the PLS model around the 3D structure of the antitussive molecule Noscapine. The red lines between the MIFs represent the GRIND descriptors.

Figure 9.13. Analysis of the GRIND descriptors produced by the crystal structures ofthe proteins allowed us to find the distances between MIFs that could match GRIND descriptors 11-23 and 13-25. On the left are...

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