Surface-integral QSPR models: local energy properties

Surface-integral models based on AM1 semiempirical molecular orbital calculations are presented for the free energies of solvation in water, n-octanol, and chloroform and for the enthalpy of solvation in water. A parametrized function of four local properties calculated at the isodensity surface (the molecular electrostatic potential, local ionization energy, electron affinity, and polarizability) is integrated over the triangulated surface area to obtain the target quantity. The resulting models give results only slightly less accurate than those reported for parametrized generalized Born/polar surface area models despite relying only on gas-phase calculations. The water and octanol free-energy models were validated by calculating the water-octanol partition coefficient for a test set of organic compounds with moderate success. The models lead to a local solvation energy, which can be projected onto the molecular isodensity surface and provides insight into "hot" areas for solvation in water or the other solvents.     

Novel enhanced applications of QSPR models: Temperature dependence of aqueous solubility

A model developed to predict aqueous solubility at different temperatures has been proposed based on quantitative structure–property relationships (QSPR) methodology. The prediction consists of two steps. The first one predicts the value of k parameter in the linear equation , where S_w is the value of solubility and T is the value of temperature. The second step uses Random Forest technique to create high‐efficiency QSPR model. The performance of the model is assessed using cross‐validation and external test set prediction. Predictive capacity of developed model is compared with COSMO‐RS approximation, which has quantum chemical and thermodynamic foundations. The comparison shows slightly better prediction ability for the QSPR model presented in this publication. © 2016 Wiley Periodicals, Inc.     

Toward robust QSPR models: Synergistic utilization of robust regression and variable elimination

Widely used regression approaches in modeling quantitative structure-property relationships, such as PLS regression, are highly susceptible to outlying observations that will impair the prognostic value of a model. Our aim is to compile homogeneous datasets as the basis for regression modeling by removing outlying compounds and applying variable selection. We investigate different approaches to create robust, outlier-resistant regression models in the field of prediction of drug molecules' permeability. The objective is to join the strength of outlier detection and variable elimination increasing the predictive power of prognostic regression models. In conclusion, outlier detection is employed to identify multiple, homogeneous data subsets for regression modeling.     

Comparative studies on some metrics for external validation of QSPR models

Quantitative structure-property relationship (QSPR) models used for prediction of property of untested chemicals can be utilized for prioritization plan of synthesis and experimental testing of new compounds. Validation of QSPR models plays a crucial role for judgment of the reliability of predictions of such models. In the QSPR literature, serious attention is now given to external validation for checking reliability of QSPR models, and predictive quality is in the most cases judged based on the quality of predictions of property of a single test set as reflected in one or more external validation metrics. Here, we have shown that a single QSPR model may show a variable degree of prediction quality as reflected in some variants of external validation metrics like Q2(F1), Q2(F2), Q2(F3), CCC, and r2(m) (all of which are differently modified forms of predicted variance, which theoretically may attain a maximum value of 1), depending on the test set composition and test set size. Thus, this report questions the appropriateness of the common practice of the "classic" approach of external validation based on a single test set and thereby derives a conclusion about predictive quality of a model on the basis of a particular validation metric. The present work further demonstrates that among the considered external validation metrics, r2(m) shows statistically significantly different numerical values from others among which CCC is the most optimistic or less stringent. Furthermore, at a given level of threshold value of acceptance for external validation metrics, r2(m) provides the most stringent criterion (especially with Δr2(m) at highest tolerated value of 0.2) of external validation, which may be adopted in the case of regulatory decision support processes.     

Global versus local QSPR models for persistent organic pollutants: balancing between predictivity and economy

Experimentally determined data on the key physicochemical parameters for halogenated congeners of persistent organic pollutants (POPs) are available only for a limited number of compounds. In the absence of experimental data, a range of computational methods can be applied to characterize those species for which experimental data is not available. One of the techniques widely used in this context is quantitative structure–property relationships (QSPR) approach. There are two ways to develop the QSPR models: using a more complex global model or fitting a simple local model that covers a specific class of chemically related compounds. The essence of the study was to investigate, if local models have significantly better explanatory and predictive ability than global models with wider applicability domains. Based on the obtained results, we concluded that whenever global models fulfill all quality recommendations by OECD, they would be applied in practice as more efficient ones in state of more time consuming procedure of modeling the particular groups of POPs one-by-one. On the contrary, local models are applicable to solve specific problems (i.e., related to only one group of POPs), when high-quality experimental data are available for a sufficient number of training and validation compounds.     

QSPR models for water solubility of ammonium hexafluorosilicates: analysis of the effects of hydrogen bonds

Structural Chemistry - In the present study, adequate quantitative structure-activity relationship (QSAR) models were developed to analyze the water solubility of some ammonium hexafluorosilicates....     

QSPR generalization of activity coefficient models for predicting vapor–liquid equilibrium behavior

Multiphase equilibrium calculations are an integral part of the design and optimization of numerous chemical processes. Several accurate experimental techniques have been developed for measuring phase equilibrium data. However, experimental techniques are time consuming and costly. Hence, a need exists for reliable thermodynamic models capable of giving a priori predictions of the phase behavior of diverse systems in the absence of experimental data.     

Conformation-dependent racemization of aspartyl residues in peptides

Biologically uncommon D-aspartyl (D-Asp) residues have been detected in proteins of various tissues of elderly humans. The presence of D-Asp has been explained as a result of the racemization of L-Asp (denoted as Asp) in the protein of inert tissues. We have previously suggested that the racemization of Asp may depend on the conformation of the peptide chain. However, the nature of the peptide conformation that affects the D-Asp formation has not yet been examined. Here we report the kinetics of Asp racemization in two model peptides, (Asp-Leu)(15) and (Leu-Asp-Asp-Leu)(8)-Asp, which form beta-sheet structures and alpha-helical structures, respectively. For the beta-sheet structures, the activation energy of racemization of Asp residues was 27.3 kcal mol(-1), the racemization rate constant at 37 degrees C was 2.14x10(-2) per year and the time required to reach a D/L ratio of 0.99 at 37 degrees C was 122.6 years as estimated from the Arrhenius equation. For the alpha-helical structures, the activation energy of racemization was 18.4 kcal mol(-1), the racemization rate constant 20.02x10(-2) per year and the time 13.1 year. These results suggest that Asp residues inserted in alpha-helical peptides are more sensitive to racemization than Asp residues inserted in peptides adopting beta-sheet structures. The results clearly indicate that the racemization rate of Asp residues in peptides depends on the secondary structure of the host peptide.