QSAR equation

The derivation of a QSAR equation involves a number of distinct stages. First, it is obviousl necessary to synthesise the compormds and determine their biological activities. Whe planning which compormds to synthesise, it is important to cover the range of propertie that may affect the activity. This means applying the data-checking and -manipulation prc cedures discussed earlier. For example, it would be unwise to make a series of coinpound with almost identical partition coefficients if this is believed to be an important property. 

In order to parameterize a QSAR equation, a quantihed activity for a set of compounds must be known. These are called lead compounds, at least in the pharmaceutical industry. Typically, test results are available for only a small number of compounds. Because of this, it can be difficult to choose a number of descriptors that will give useful results without htting to anomalies in the test set. Three to hve lead compounds per descriptor in the QSAR equation are normally considered an adequate number. If two descriptors are nearly col-linear with one another, then one should be omitted even though it may have a large correlation coefficient. 

In QSAR equations, n is the number of data points, r is the correlation coefficient between observed values of the dependent and the values predicted from the equation, is the square of the correlation coefficient and represents the goodness of fit, is the cross-validated (a measure of the quality of the QSAR model), and s is the standard deviation. The cross-validated (q ) is obtained by using leave-one-out (LOO) procedure [33]. Q is the quality factor (quality ratio), where Q = r/s. Chance correlation, due to the excessive number of parameters (which increases the r and s values also), can. 

Fischer statistics (F) Fischer statistics (F) is the ratio between explained and unexplained variance for a given number of degree of freedom. The larger the F value the greater the probability that the QSAR equation is significant. The F values obtained for these QSAR models are from 17.622 to 283.714, which are statistically significant at the 95% level. 

Table 17 Ames mutagenicity data for Salmonella typhimurium TA100 and physicochemical parameters for A-acyloxy-A-alkoxyamides together with predicted activities according to QSAR (Equation 4)... 

Multivariate analysis incorporating log P, pKA and steric effects E, E2 and E for para substituents on the benzamide, benzyloxy and benzoyloxy side chains afforded a QSAR (Equation 3) ... 

Membrane-Interaction (MI)-QSAR approach developed by Iyer et al. was used to develop predictive models of some organic compounds through BBB, and to simulate the interaction of a solute with the phospholipide-rich regions of cellular membranes surrounded by a layer of water. Molecular dynamics simulations were used to determine the explicit interaction of each test compound with the DMPC-water model (a model of dimyristoylphosphatidylcholine membrane monolayer, constructed using the software Material Studio according to the work done by van der Ploeg and Berendsen). Six MI-QSAR equations were constructed (Eqs. 74-79) ... 

KARMA describes the interactions for enzyme-ligand binding using QSAR equations and parameters, and the structural information of the congener data. These interactions, with illustrative examples, are shown below ... 

KarmaData contains information which the user enters, e.g., QSAR equations, congener set, as well as information about previously studied enzyme-ligand binding complexes. KarmaData contains several classes and subclasses. For example, in KarmaData, there is a class called proteins, a subclass in proteins called dehydrogenase, a particular member of dehydrogenase c led DHFR, and a specific instance of DHFR called chicken (vide ir a). Chicken DHFR contains those attributes which are specific to itself, and inherits properties from units DHFR, dehydrogenase, and proteins. 

Generic rules are based on the QSAR equations and their coefficients. Forward chaining using these rules yields basic characteristics for the receptor site model. For instance, an abstracted generic rule may take the form ... 

Considerable literature developed around the ability of numerical indices derived from graph theoretical considerations to correlate with S AR data. This was a source of mystery to me for some time. A colleague, loan Motoc, from Romania, with experience in this arena and a very strong intellect, helped me understand the ability of various indices to be useful parameters in QSAR equations [19-21]. loan correlated various indices with more physically relevant (at least to me) variables such as surface area and molecular volume. Since computational time was at a premium during the early days of QSAR and such indices could be calculated with minimal computations, they played a useful role and continue to be used. As a chemist, however, I am much more comfortable with parameters such as surface area or volume. 

This procedure assessed whether some of the different descriptors used by different equations were intercorrelated and, therefore, interchangeable [59]. The remaining diverse QSAR equations were further classified by size (number of descriptors they include). The best equations of each encountered size were kept for final validation with the VS molecules and for further analysis. Consensus models featuring average predictions over these equations were also generated and validated. We focus here on the discussion of the minimalist overlay-independent and overlay-based QSAR models, each including only six descriptors, and refer to the optimal consensus model of the overlay-based QSAR approach families for comparative purposes. 

Wei, D.T., Meadows, J.C., and Kellogg, G.E. Effects of entropy on QSAR equations for HlV-1 protease 1. Using hydropathic binding descriptors. 

The close agreement between the experimental and calculated (Equation 9) ratios of 18 2/18 3 support exclusion of the 4-hydroxylphenyl analogue from the calculations. Examination of Equation 9 shows an interdependence between the biological activity and the hydrophobic properties of the chemical used, commonly found with many QSAR equations. This interdependent relationship is determined by the and terms, respectively. These terms control phenomena of hydrophobic interactions with receptors and phenomena of transport and distribution within the total biological systems. The occurrence of squared terms of the hydrophobic parameter in structure-activity correlations has been explained on the assumption that the compound has to penetrate several lipophilic-hydrophilic barriers or compartments on its way to the site of action (16, 17). This is consistent with the uptake of pyridazinones by roots and sbsequent translocation to the shoots (chloroplast) as the site of action (13). 

Optimization of biological properties in a series of miticidal and mite ovicidal 2-aryl-l,3-cycloalkanediones, Ia,b, and enol esters, II, was achieved through analog synthesis and testing supported by the development of quantitative structure/ activity trends during the course of the project. QSAR equations developed during an initial phase provided the basis for both... 

---