Quantitative Structure Activity Relationship (QSAR) is a term describing a variety of approaches that are of substantial interest for chemistry. This method can be defined as indirect molecular design by the iterative sampling of the chemical compounds space to optimize a certain property and thus indirectly design the molecular structure having this property. However, modeling the interactions of chemical molecules in biological systems provides highly noisy data, which make predictions a roulette risk. In this paper we briefly review the origins for this noise, particularly in multidimensional QSAR. This was classified as the data, superimposition, molecular similarity, conformational, and molecular recognition noise. We also indicated possible robust answers that can improve modeling and predictive ability of QSAR, especially the self-organizing mapping of molecular objects, in particular, the molecular surfaces, a method that was brought into chemistry by Gasteiger and Zupan.

For 4-cyano-3-fluorophenyl 4-butylbenzoate (4CFPB), the process of the crystallization of the CrII phase was studied in microscopic (POM), calorimetric (DSC), and dielectric (BDS) nonisothermal experiments with various (0.5−50 K/min) heating of the metastable nematic phase obtained from its glass. Growth of areas of crystal CrII in the microscopic texture of nematic phase during heating allows estimation of degree of crystallinity D(T) vs temperature curves similar to these obtained basing on DSC heat flow curves and for slow heating with help of dielectric relaxation (BDS) method. Two types of CrII crystallization mechanisms seem to be identified: (1) strong ϕ dependence on temperature of full crystallization T c (ϕ) and half time of crystallization t 1/2 (ϕ) on slow heating up to 5 K/min points to diffusioncontrolled mechanism with the energy barrier 57 kJ/mol, and (2) small effect of faster heating on T c (ϕ) and t 1/2 (ϕ) seems to illustrate thermodynamic mechanism with energy barrier 180 kJ/mol. The scenario of two mechanisms of CrII crystallization is in agreement with the results of new method proposed by Mo et al., using combination of Avrami and Ozawa equations for description of nonisothermal crystallization. In addition to crystallization of CrII of 4CFPB, at higher temperature range CrII−CrI transformation to a stable CrI crystal was digitalized based on microscopic and DCS results for heating at 1 K/min. ■ INTRODUCTIONThe well-known crystallization phenomenon is still not clearly described as it depends on many factors like a type of nucleation of crystal grains, a nucleation rate, and a rate of growth of crystallites in the melt substance. 1,2 Calculations of absolute nucleation and growth rates are difficult, but each substance has its own temperature ranges where nucleation and growth are favorable. Usually, the rate I(T) of nucleation has its maximum at lower temperature than the maximum for the rate G(T) of crystal growth. 2 Moreover, both rates may be complicated functions of temperature and of details of the experiment used (e.g., cooling rate). The driving force of overall crystallization depends on viscosity to entropy relationships and their temperature changes. 3 Lower temperature parts of I(T) and G(T) curves reflect growing viscosity (transport parts), while the higher temperature parts are the results of larger diffusivity/molecular mobility (thermodynamic parts) in the substance under study. 2 If these curves have no temperature range in common, no crystallization is detected on cooling. Instead, vitrification is observed, no matter how slowly the temperature is decreased. Then, crystallization is expected on heating. In isothermal studies of crystallization kinetics, degree D(t) of crystallinity (or crystallization ratio) is described in terms of Avrami model 4,5Linear dependence of log{−ln[1 − D(t)]} vs log t is expected: slope n A describes the dimensionality of the process and the nucleation mode (instantaneous, prolonged in time, 1-, 2-or 3-dimensional etc.), and the k parameter d...

In the current paper we present a receptor-independent 4D-QSAR method based on self-organizing mapping (SOM-4D-QSAR) and in particular focus on its pharmacophore mapping ability. We use a novel stochastic procedure to verify the predictive ability of the method for a large population of 4D-QSAR models generated. This systematic study was conducted on a series of benzoic acids, azo dyes, and steroids that bind aromatase. We show that the 4D-QSAR method coupled with IVE-PLS provides a very stable and predictive modeling technique. The method enables us to identify the molecular motifs contributing the most to the fiber-dye affinity and the aromatase enzyme binding activity of the steroid. However, the method appeared much less effective for the benzoic acid series, in which the efficacy was limited by electronic effects strictly correlated to a single conformer.

In the current work we investigated 3D-QSAR data by the use of the coupled leave-several-out (LSO) and leave-one-out (LOO) cross-validation (CV) procedures. We verified the above mentioned scheme using both simulated data and real 3D QSAR data describing a series of CoMFA steroids, heterocyclic azo dyes and styrylquinoline HIV integrase inhibitors. Unlike in standard analyses, this technique characterizes individual method not by a single performance metrics but screens a whole possible modeling space by sampling different molecules into the training and test sets, respectively. This allowed us for the discussion of the information included in the estimators validating cross-validation procedures, as well as the comparison of the efficiency of several 3D QSAR schemes, in particular, Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Surface Analysis (CoMSA). Moreover, it allows one to acquire some general knowledge about predictive and modeling ability in 3D QSAR method.

We conducted a systematic study of the performance of the 3D- and 4D-QSAR schemes in modeling steric and electronic effects. In particular, we compared the CoMFA and Hopfinger's 4D-QSAR schemes, which apply completely different concepts for the generation of the molecular data used for modeling QSAR. Hence, we attempted to predict the pK(a) values of (o-, m-, and p-)benzoic acids which were divided into three subseries in order to simulate different levels of steric and electronic control. The steroids binding to CBG were used as a benchmark series where biological activity is limited by shape factors. Although individual models differ depending upon the individual scheme, generally, both CoMFA and 4D-QSAR appeared to provide comparable results, irrespective of the differences in the coding schemes used for the description. Moreover, a new 4D-QSAR scheme involving a self-organizing neural network was designed. Generally, the SOM scheme that we designed performs comparably to the grid scheme; however, it provides better results for the charge type descriptors, and the robust neuron architecture allows for the decrease of the influence of the molecular superimposition mode.