Modeling with physicochemical descriptors

In Table 5.1, we present a list of the main physicochemical and structural properties associated with the descriptors included in the 2D QSAR models discussed above. Of course, we did some generalizations in an attempt to refer different parameters and descriptors to the same property, but the effort was devoted at identifying the smallest number of significant features positively or negatively correlated to the hERG blockade by small molecules. Examining the properties... 

Osterberg and Norinder [42] further analyzed a subset of the ATPase data published by Litman et al. [39]. The ATPase activity values were correlated by multivariate statistics with calculated descriptors (MolSurf descriptors) related to physicochemical properties suchaslipophilicity, polarity, polarizability and hydrogen bonding. After exclusion of one outlier and large molecules such as valinomycin, gramicidin S and so on, which were not handled by the MolSurf software, only 21 compounds were included in the study. Two models were derived model 1, based on... 

It is interesting to note that various QSAR/QSPR models from an array of methods can be very different in both complexity and predictivity. For example, a simple QSPR equation with three parameters can predict logP within one unit of measured values (43) while a complex hybrid mixture discriminant analysis-random forest model with 31 computed descriptors can only predict the volume of distribution of drugs in humans within about twofolds of experimental values (44). The volume of distribution is a more complex property than partition coefficient. The former is a physiological property and has a much higher uncertainty in its experimental measurements while logP is a much simpler physicochemical property and can be measured more accurately. These and other factors can dictate whether a good predictive model can be built. 

The chapter is set out in the same order in which QSAR models are generally constructed starting with the selection of compounds for modeling, collection of response data, assembly of physicochemical descriptor data, data reduction, or selection, and the construction and interpretation of the models. 

How do we set about variable selection One obvious approach is to examine the pair-wise correlations between the response and the physicochemical descriptors. One form of model building, forward stepping multiple regression, begins by choosing the descriptor that has the highest correlation with a response variable. If the response is a categorical variable such as toxic/non-toxic,... 

In a follow-up of our modeling studies on thiazolidine-based HIV-1 RT inhibitors, we have synthesized some thiazolidin-4-ones, metathiazanones for this activity. The QSAR studies of these compounds with physicochemical and quantum chemical descriptors have highlighted the importance of PMIZ... 

Log P can be used as an additional parameter, in combination with other descriptors. For example, neural network models developed by Liu and So and Goller et al use log P in combination with topological and quantum-chemical descriptors. Many methods do not use log R as a descriptor. These methods have been described in several reviews. However, there is a clear relationship between these two physicochemical properties, namely log P and aqueous solubility. 

Most of the commercial molecular modeling systems also provide some property calculations, which range from simply calculating the polar surface area of a structure to a full range of topological and physicochemical descriptors. These may be based on fragment additivity, like most of the programs mentioned above, or they may involve correlations with quantum mechanical or even molecular dynamics-based calculations. 

I = 21 y = 0.394 r = 0.9476 F = 49.8 where log P is the hydrophobicity, bondrefr is the molecular refractivity, delta is the submolecular polarity parameter, ind indicator variable (0 for heterocyclics and 1 for benzene derivatives). Calculations indicated that PBD-coated alumina behaves as an RP stationary phase, the bulkiness and the polarity of the solute significantly influencing the retention. The separation efficiency of PBD-coated alumina was compared with those of other stationary phases for the analysis of Catharanthus alkaloids. It was established that the pH of the mobile phase, the concentration and type of the organic modifier, and the presence of salt simultaneously influence the retention. In this special case, the efficiency of PBD-coated alumina was inferior to that of ODS. The retention characteristics of polyethylene-coated alumina (PE-Alu) have been studied in detail using various nonionic surfactants as model compounds.It was found that PE-Alu behaves as an RP stationary phase and separates the surfactants according to the character of the hydrophobic moiety. The relationship between the physicochemical descriptors of 25 aromatic solutes and their retention on PE-coated silica (PE-Si) and PE-Alu was elucidated by stepwise regression analysis. 

Description A QSAR toolkit with descriptor generation (topological, geometrical, electronic, and physicochemical descriptors), variable selection, regression and artificial neural network modelling. 

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