Application of quantum topological molecular similarity descriptors in QSPR study of the O-methylation of substituted phenols

Reaction rate constant: a theoretical description from local temperature

Application of various descriptors based on electron density and its associated quantities to quantify chemical reactivity within the conceptual density functional theory has recently come into spotlight. Among others and particularly relevant to our study, local temperature based on electron density as well as kinetic energy density, as a measure of the kinetic energy of an electron moving in the Kohn-Sham potential of systems, should be mentioned. In this work, we propose to use the local temperature for describing the reaction rate constant, where our main idea originates from the point that the smaller the local temperature at the reaction center, the easier the electron removal, leading to a larger rate constant. On the basis of theoretical considerations, it is proved that the rate constant variations caused by the substituent effects can well be proportional to the local temperature at the reaction center. In order to numerically validate our proposed approach, we have taken the phenol derivatives with the available experimental rate constants of their O-methylation reaction as working models. The reason for this choice is that one of the most versatile approaches for labeling biologically active compounds with the 11C nuclide for positron emission tomography (PET) is methylation by methyl iodide including 11C nuclide, [11C]MeI, where methylation of phenolic oxygen with [11C]MeI is utilized to label some important tracers for PET studies. Our results unveil that the local temperature changes at the reaction center of the aforementioned reaction are reasonably correlated with the rate constant variations. Hopefully, incorporating the proposed correlations between the local temperature and the kinetics data into a computer control algorithm not only provides a simple tool for predicting the rate constant of the O-methylation reaction for other substituted phenols, but also, as a part of the chemical artificial intelligence, the optimum [11C]MeI labeling conditions for a wide variety of phenol derivatives can be controlled.

Reply to the 'Comment on "Fullerene-based materials for solar cell applications: design of novel acceptors for efficient polymer solar cells--a DFT study"' by D. S. Sabirov, A. O. Terentyev and I. S. Shepelevich, Phys. Chem. Chem. Phys., 2016, DOI

It is possible to predict the photovoltaic performance of fullerene derivatives based on their electronic/magnetic properties.

Models for anti-tumor activity of bisphosphonates using refined topochemical descriptors

An in silico approach comprising of decision tree (DT), random forest (RF) and moving average analysis (MAA) was successfully employed for development of models for prediction of anti-tumor activity of bisphosphonates. A dataset consisting of 65 analogues of both nitrogen-containing and non-nitrogen-containing bisphosphonates was selected for the present study. Four refinements of eccentric distance sum topochemical index termed as augmented eccentric distance sum topochemical indices 1-4 [formula: see text] have been proposed so as to significantly augment discriminating power. Proposed topological indices (TIs) along with the exiting TIs (>1,400) were subsequently utilized for development of models for prediction of anti-tumor activity of bisphosphonates. A total of 43 descriptors of diverse nature, from a large pool of molecular descriptors, calculated through E-Dragon software (version 1.0) and an in-house computer program were selected for development of suitable models by employing DT, RF and MAA. DT identified two TIs as most important and classified the analogues of the dataset with an accuracy of 97% in training set and 90.7% in tenfold cross-validated set. Random forest correctly classified the analogues with an accuracy of 89.2%. Four independent models developed through MAA predicted the activity of analogues of the dataset with an accuracy of 87.6% to 89%. The statistical significance of proposed models was assessed through intercorrelation analysis, specificity, sensitivity and Matthew's correlation coefficient. The proposed models offer a vast potential for providing lead structures for development of potent anti-tumor agents for treatment of cancer that has spread to the bone.

Prediction of interaction energies of substituted hydrogen-bonded Watson-Crick cytosine:guanine(8X) base pairs

We investigated the variation in the interaction energy between the Watson-Crick hydrogen-bonded DNA base pairs guanine and cytosine (G(8X):C), where guanine is substituted in the C8 position by 37 different functional groups. Base pairs were optimized at the B3LYP/6-311+G(2d,p) level. A base pair complex containing a more strongly electron-withdrawing group remarkably forms a more stable base pair with C. Multivariate linear regression provided a quantitative relationship between the interaction energies and descriptors generated by the quantum chemical topology (QCT) approach. The descriptors were sampled from the monomers only, not the supermolecular base pair complexes. A model with r2 = 0.96 and a root-mean-square (rms) value of 0.6 kJ/mol was obtained for a training set of 28 base pair complexes. The model was tested by an external test set of 9 complexes, yielding r2 = 0.99 and an rms value of 0.2 kJ/mol. The results indicated that the bonds C6=O6 and N2-H2 at the hydrogen-bonded frontier of the guanine derivatives play an important role in transmitting the substituent effects. A linear correlation between substitution energies and Hammett constants (sigma(m)) was also obtained for all 37 substituents, yielding r2 = 0.82 and an rms value of 1.2 kJ/mol. The model based on QCT descriptors can therefore be used for the prediction of the interaction energy of the base pair G(8x):C, strictly based on data for the G(8x) monomers only.