Modeling with quantum chemical descriptors

Binding Events Modeled with Quantum Chemical Descriptors. Inhibition of the Hill reaction (spinach chloroplasts) correlates with the energy level of the highest occupied MO on amide nitrogen for a series of piperldinoacetanilides (139). Electron density on amide nitrogen also correlates well with E,... 

Rorije, E., Langenberg, J.H., Richter, J., and Peijnenburg, W.J.G.M., Modeling reductive dehalogenation with quantum-chemically derived descriptors, SAP QSAR Environ, Res., 4, 237-252, 1995. 

In our contribution we have focused the discussion on descriptors. The understanding of descriptors is essential for transparency of models and can also lead to mechanistic interpretation of models. Several questions are associated with descriptors. First of all, nowadays thousand of descriptors are defined and can be easily calculated with available software and the first question is how to the select the most relevant descriptors. The topological descriptors are sometimes promising, but there is no clear physicochemical interpretation for them. 3D molecular structure is a problematic quantity as it depends on the media where the molecule is, or on the method of determination. Quantum chemical descriptors, which have a clear physicochemical interpretation, are difficult to calculated. In the cases studies we have addressed some of those questions. We have discussed the sensitivity of the models, and particularly predictions, to descriptors used. According to the critical review of Snyder and Smith [87] on QSAR models for mutagenicity prediction a lot of work still remains to be done. 

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. 

In another recent study, QSAR models were developed using quantum-chemical descriptors to describe the toxic influence of polychlorinated organic compounds on the rainbow trout (Oncorhynchus mykiss). The logarithm of the bioconcentration factor (BCF) was best correlated with the AM 1 -calculated a-polarizability, energies of the frontier orbitals, and the core-core repulsion energy (CCR), as follows [121] ... 

In particular, combinations of PARASURF descriptors with molecular surface-based descriptors were of interest. Descriptors like the local ionization potential or the local electron affinity, provided by PARASURF, should capture aspects of chemical reactivity, while surface or pharmacophore-related descriptors could have the potential to describe the recognition of the molecule by cytochromes. Hence, various descriptor sets were combined with quantum-chanical descriptors from PARASURF yielding improved models. For example, the sole set of 2D-MOE descriptors shows an f of 0.6 for the training set, while rises to 0.68 for the MOE/PARASURF combinations. 

Vectors A series of scalars can be arranged in a column or in a row. Then, they are called a column or a row vector. If the elements of a column vector can be attributed to special characteristics, e.g., to compounds, then data analysis can be completed. The chemical structures of compounds can be characterized with different numbers called descriptors, variables, predictors, or factors. For example, toxicity data were measured for a series of aromatic phenols. Their toxicity can be arranged in a column arbitrarily Each row corresponds to a phenolic compound. A lot of descriptors can be calculated for each compound (e.g., molecular mass, van der Waals volume, polarity parameters, quantum chemical descriptors, etc.). After building a multivariate model (generally one variable cannot encode the toxicity properly) we will be able to predict toxicity values for phenolic compounds for which no toxicity has been measured yet. The above approach is generally called searching quantitative structure - activity relationships or simply QSAR approach. 

Two models of practical interest using quantum chemical parameters were developed by Clark et al. [26, 27]. Both studies were based on 1085 molecules and 36 descriptors calculated with the AMI method following structure optimization and electron density calculation. An initial set of descriptors was selected with a multiple linear regression model and further optimized by trial-and-error variation. The second study calculated a standard error of 0.56 for 1085 compounds and it also estimated the reliability of neural network prediction by analysis of the standard deviation error for an ensemble of 11 networks trained on different randomly selected subsets of the initial training set [27]. 

Roy and Leonard [183] have also presented QSAR models for the HIV-1 RT inhibitory activity of the thiazolidinones listed in Tables 16 and 17 along with some more similar analogues (Table 18) [184-187] using the hydrophobic-ity and molar refractivity, quantum chemical and topological and indicator parameters as descriptors. In this, the 3-pyridyls/phenyls (Tables 16 and 17) and 3-(pyrimidin-2-yls) (Table 18) [186] have become part of the dataset. Additionally, four compounds with the thiazolidin-4-thione nucleus have been included in the dataset. In Fujita-Ban [188] and mixed (Hansch and Fujita-Ban) approaches 7 to 17 descriptor models have been discovered for the cytopathicity effect (EC50) and cytotoxic effect (CC50) of the compounds. The following equations show the minimum descriptor models for each activity from this study. 

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