On Degree Based Topological Indices of TiO2 Crystal via M-Polynomial

Topological indices (TI) (descriptors) of a molecular graph are very much useful to study various physiochemical properties. It is also used to develop the quantitative structure-activity relationship (QSAR), quantitative structure-property relationship (QSPR) of the corresponding chemical compound. Various techniques have been developed to calculate the TI of a graph. Recently a technique of calculating degree-based TI from M-polynomial has been introduced. We have evaluated various topological descriptors for 3-dimensional TiO2 crystals using M-polynomial. These descriptors are constructed such that it contains 3 variables (m, n and t) each corresponding to a particular direction. These 3 variables facilitate us to deeply understand the growth of TiO2 in 1 dimension (1D), 2 dimensions (2D), and 3 dimensions (3D) respectively. HIGHLIGHTS Calculated degree based Topological indices of a 3D crystal from M-polynomial A relation among various Topological indices is established geometrically Variations of Topological Indices along three dimensions (directions) are shown geometrically Harmonic index approximates the degree variation of oxygen atom

Systematic Modeling, Prediction, and Comparison of Domain–Peptide Affinities: Does it Work Effectively With the Peptide QSAR Methodology?

The protein–protein association in cellular signaling networks (CSNs) often acts as weak, transient, and reversible domain–peptide interaction (DPI), in which a flexible peptide segment on the surface of one protein is recognized and bound by a rigid peptide-recognition domain from another. Reliable modeling and accurate prediction of DPI binding affinities would help to ascertain the diverse biological events involved in CSNs and benefit our understanding of various biological implications underlying DPIs. Traditionally, peptide quantitative structure-activity relationship (pQSAR) has been widely used to model and predict the biological activity of oligopeptides, which employs amino acid descriptors (AADs) to characterize peptide structures at sequence level and then statistically correlate the resulting descriptor vector with observed activity data via regression. However, the QSAR has not yet been widely applied to treat the direct binding behavior of large-scale peptide ligands to their protein receptors. In this work, we attempted to clarify whether the pQSAR methodology can work effectively for modeling and predicting DPI affinities in a high-throughput manner? Over twenty thousand short linear motif (SLiM)-containing peptide segments involved in SH3, PDZ and 14-3-3 domain-medicated CSNs were compiled to define a comprehensive sequence-based data set of DPI affinities, which were represented by the Boehringer light units (BLUs) derived from previous arbitrary light intensity assays following SPOT peptide synthesis. Four sophisticated MLMs (MLMs) were then utilized to perform pQSAR modeling on the set described with different AADs to systematically create a variety of linear and nonlinear predictors, and then verified by rigorous statistical test. It is revealed that the genome-wide DPI events can only be modeled qualitatively or semiquantitatively with traditional pQSAR strategy due to the intrinsic disorder of peptide conformation and the potential interplay between different peptide residues. In addition, the arbitrary BLUs used to characterize DPI affinity values were measured via an indirect approach, which may not very reliable and may involve strong noise, thus leading to a considerable bias in the modeling. The Rprd2 = 0.7 can be considered as the upper limit of external generalization ability of the pQSAR methodology working on large-scale DPI affinity data.

On the Spectroscopic Analyses of Polytetrafluoroethylene Coated with Nano ZnO and SiO2

On polytetrafluoroethylene (PTFE) polymer nanocomposites coated with basically two metal oxides (MOs), SiO2 and ZnO, as well as a mixture of the two MOs, density functional theory (DFT) computations were performed. The B3LYPL/LAN2DZ model was used to evaluate PTFE polymer nano composites suggested model structures. The physical and electrical properties of PTFE modified on surface with ZnO and SiO2 coated layer by layer change Total dipole moment (TDM) and HOMO/LUMO band gap energy ∆Eto be 13.0082 Debye and 0.6889 eV, respectively. Moreover, TDM and band gap energy (∆E) improved to 10.6053 Debye and 0.2727 eV, respectively, when the nanofiller was increased to 8 atoms. In addition, the results of the Molecular Electrostatic Potential (MESP) and the Quantitative Structure Activity Relationship (QSAR) showed that PTFE coated with ZnO and SiO2 improved electrical characteristics and thermal stability. As PTFE coated with ZnO and SiO2 layer by layer, all stability characteristics, including electrical and thermal stability, were enhanced. The improved PTFE can be used as a corrosion-inhibiting layer for astronaut suits, according to the predicted results.

Computational Estimation of the Acidities of Pyrimidines and Related Compounds

Pyrimidines are key components in the genetic code of living organisms and the pyrimidine scaffold is also found in many bioactive and medicinal compounds. The acidities of these compounds, as represented by their pKas, are of special interest since they determine the species that will prevail under different pH conditions. Here, a quantum chemical quantitative structure–activity relationship (QSAR) approach was employed to estimate these acidities. Density-functional theory calculations at the B3LYP/6-31+G(d,p) level and the SM8 aqueous solvent model were employed, and the energy difference ∆EH2O between the parent compound and its dissociation product was used as a variation parameter. Excellent estimates for both the cation → neutral (pKa1, R2 = 0.965) and neutral → anion (pKa2, R2 = 0.962) dissociations were obtained. A commercial package from Advanced Chemical Design also yielded excellent results for these acidities.

Construction of a prediction model for drug removal rate in hemodialysis based on chemical structures

In designing drug dosing for hemodialysis patients, the removal rate (RR) of the drug by hemodialysis is important. However, acquiring the RR is difficult, and there is a need for an estimation method that can be used in clinical settings. In this study, the RR predictive model was constructed using the RR of known drugs by quantitative structure–activity relationship (QSAR) analysis. Drugs were divided into a model construction drug set (75%) and a model validation drug set (25%). The RR was collected from 143 medicines. The objective variable (RR) and chemical structural characteristics (descriptors) of the drug (explanatory variable) were used to construct a prediction model using partial least squares (PLS) regression and artificial neural network (ANN) analyses. The determination coefficients in the PLS and ANN methods were 0.586 and 0.721 for the model validation drug set, respectively. QSAR analysis successfully constructed dialysis RR prediction models that were comparable or superior to those using pharmacokinetic parameters. Considering that the RR dataset contains potential errors, we believe that this study has achieved the most reliable RR prediction accuracy currently available. These predictive RR models can be achieved using only the chemical structure of the drug. This model is expected to be applied at the time of hemodialysis. Graphic Abstract