The index of ideality of correlation: hierarchy of Monte Carlo models for glass transition temperatures of polymers

Paradox of 'ideal correlations': improved model for air half-life of persistent organic pollutants

The persistence of organic pollutants is an important environmental property due to the extended possibility to have an impact of corresponding substances. In many cases, the experimental values of the thousands of contaminants are missing. The object of the study is novel computational modelling for air pollutions. Quantitative structure-property relationship (QSPR) for air half-life has been built using the Monte Carlo method with applying the index of ideality of correlation (IIC). The basis of the predictive model of air half-life is the representation of the molecular structure by simplifying molecular input-line entry system (SMILES) and numerical data on the above endpoint (expressed by hours) converted to a decimal logarithm. The statistical quality of the model has been checked up with different validation metrics and is quite good. Paradoxically, the improvement of the statistical quality via the IIC for the validation set is done in detriment to the training set. The new model has performed better than those obtained previously on the same set of compounds, for the prediction of new compounds in the validation set. Some semi-quantitative indicators for the mechanistic interpretation of the model are suggested.

QSPR modelling for intrinsic viscosity in polymer-solvent combinations based on density functional theory

Linear and nonlinear quantitative structure-property relationship (QSPR) models were developed based on a dataset with 65 polymer-solvent combinations. Seven quantum chemical descriptors, dipole moment, hardness, chemical potential, electrophilicity index, total energy, HOMO and LUMO orbital energies, were calculated with density functional theory at the B3LYP/6-31 G(d) level for polymers and solvents. Considering the strong correlation between intrinsic viscosity and weight, size, shape as well as topological structure of polymers and solvents, topological descriptors were also applied in this work. Meanwhile, the most appropriate polymer structure representation was investigated by considering 1-5 monomeric repeating units. The molecular descriptors were first screened by using the genetic algorithms-multiple linear regression (GA-MLR), with coefficient of determinations (r2) of 0.78 and 0.83 for the training set and the prediction set, respectively. The support vector machine model (SVM) model based on the selected descriptors subset showed a r2 value of 0.95 for the training set and 0.93 for the prediction set. All statistical results suggest that the established QSPR models have good predictability. Furthermore, a new test set obtained from the literature was used for further validation. The r2 values were 0.81 for the MLR model and 0.90 for the SVM model.

The Monte Carlo Method as a Tool to Build up Predictive QSPR/QSAR

Background:

The Monte Carlo method has a wide application in various scientific researches. For the development of predictive models in a form of the quantitative structure-property / activity relationships (QSPRs/QSARs), the Monte Carlo approach also can be useful. The CORAL software provides the Monte Carlo calculations aimed to build up QSPR/QSAR models for different endpoints.

Methods:

Molecular descriptors are a mathematical function of so-called correlation weights of various molecular features. The numerical values of the correlation weights give the maximal value of a target function. The target function leads to a correlation between endpoint and optimal descriptor for the visible training set. The predictive potential of the model is estimated with the validation set, i.e. compounds that are not involved in the process of building up the model.

Results:

The approach gave quite good models for a large number of various physicochemical, biochemical, ecological, and medicinal endpoints. Bibliography and basic statistical characteristics of several CORAL models are collected in the present review. In addition, the extended version of the approach for more complex systems (nanomaterials and peptides), where behaviour of systems is defined by a group of conditions besides the molecular structure is demonstrated.

Conclusion:

The Monte Carlo technique available via the CORAL software can be a useful and convenient tool for the QSPR/QSAR analysis.

Whether the Validation of the Predictive Potential of Toxicity Models is a Solved Task?

Different kinds of biological activities are defined by complex biochemical interactions, which are termed as a "mathematical function" not only of the molecular structure but also for some additional circumstances, such as physicochemical conditions, interactions via energy and information effects between a substance and organisms, organs, cells. These circumstances lead to the great complexity of prediction for biochemical endpoints, since all "details" of corresponding phenomena are practically unavailable for the accurate registration and analysis. Researchers have not a possibility to carry out and analyse all possible ways of the biochemical interactions, which define toxicological or therapeutically attractive effects via direct experiment. Consequently, a compromise, i.e. the development of predictive models of the above phenomena, becomes necessary. However, the estimation of the predictive potential of these models remains a task that is solved only partially. This mini-review presents a collection of attempts to be used for the above-mentioned task, two special statistical indices are proposed, which may be a measure of the predictive potential of models. These indices are (i) Index of Ideality of Correlation; and (ii) Correlation Contradiction Index.