QSPR modeling of the half-wave potentials of benzoxazines by optimal descriptors calculated with the SMILES

Density function theory calculation to study the oxidation potential of electron-donating compounds; affirming the oxidation mechanism by NICS calculations

The manuscript describes a method for understanding the correlation of structural features and first oxidation potentials [Formula: see text] of electron-donating compounds (EDCs) with tetrathiafulvalene (TTF), dithiadiazafulvalenes (DTDAF), and tetraazafulvalene (TAF) frameworks. The density functional theory (DFT) procedure at B3LYP (6-31 + g(d)) was used for geometric optimization, given the large dimensions of the molecules studied, and their high structural similarity. First of all, the correlation between the oxidation potential and the highest occupied molecular orbital (HOMO) energy level as an effective quantum chemical descriptor was examined. Then, nucleus-independent chemical shifts (NICSs) calculation was applied to affirm the oxidation mechanism and interpret the effect of replacing the sulfur atoms by nitrogen, on the oxidation process. Finally, a more comprehensive investigation of structural features that affect the oxidation potential, topological, geometrical, constitutional, as well as, electrostatic, charged partial surface area, quantum-chemical, molecular orbital, and thermodynamic descriptors was calculated. A predictive model was developed based on the genetic algorithm multivariate linear regression (GA-MLR). There was an outstanding agreement between the theoretical and the experimental values obtained for the first oxidation potentials of the test set (Q²_Ext = 0.981).

A novel type of organometallic 2-R-2,4-dihydro-1H-3,1-benzoxazine with R = [M(η5-C5H4)(CO)3] (M = Re or Mn) units. Experimental and computational studies of the effect of substituent R on ring-chain tautomerism

Novel 2-cyrhetrenyl and cymantrenyl-2,4-dihydro-1H-3,1-benzoxazines.

QSPR/QSAR Analyses by Means of the CORAL Software

In this chapter, the methodology of building up quantitative structure—property/activity relationships (QSPRs/QSARs)—by means of the CORAL software is described. The Monte Carlo method is the basis of this approach. Simplified Molecular Input-Line Entry System (SMILES) is used as the representation of the molecular structure. The conversion of SMILES into the molecular graph is available for QSPR/QSAR analysis using the CORAL software. The model for an endpoint is a mathematical function of the correlation weights for various features of the molecular structure. Hybrid models that are based on features extracted from both SMILES and a graph also can be built up by the CORAL software. The conceptually new ideas collected and revealed through the CORAL software are: (1) any QSPR/QSAR model is a random event; and (2) optimal descriptor can be a translator of eclectic information into an endpoint prediction.

QSPR/QSAR Analyses by Means of the CORAL Software

In this chapter, the methodology of building up quantitative structure—property/activity relationships (QSPRs/QSARs)—by means of the CORAL software is described. The Monte Carlo method is the basis of this approach. Simplified Molecular Input-Line Entry System (SMILES) is used as the representation of the molecular structure. The conversion of SMILES into the molecular graph is available for QSPR/QSAR analysis using the CORAL software. The model for an endpoint is a mathematical function of the correlation weights for various features of the molecular structure. Hybrid models that are based on features extracted from both SMILES and a graph also can be built up by the CORAL software. The conceptually new ideas collected and revealed through the CORAL software are: (1) any QSPR/QSAR model is a random event; and (2) optimal descriptor can be a translator of eclectic information into an endpoint prediction.

Advancing risk assessment of engineered nanomaterials: application of computational approaches

Nanotechnology that develops novel materials at size of 100nm or less has become one of the most promising areas of human endeavor. Because of their intrinsic properties, nanoparticles are commonly employed in electronics, photovoltaic, catalysis, environmental and space engineering, cosmetic industry and - finally - in medicine and pharmacy. In that sense, nanotechnology creates great opportunities for the progress of modern medicine. However, recent studies have shown evident toxicity of some nanoparticles to living organisms (toxicity), and their potentially negative impact on environmental ecosystems (ecotoxicity). Lack of available data and low adequacy of experimental protocols prevent comprehensive risk assessment. The purpose of this review is to present the current state of knowledge related to the risks of the engineered nanoparticles and to assess the potential of efficient expansion and development of new approaches, which are offered by application of theoretical and computational methods, applicable for evaluation of nanomaterials.

QSAR modelling toxicity toward rats of inorganic substances by means of CORAL

CORAL (‘CORrelation And Logic’) is freeware available on the Internet www.insilico.eu/coral The aim of this program is to establish a correlation between an endpoint and descriptors calculated with a simplified molecular input line entry system (SMILES). Three models calculated by CORAL for toxicity towards rat (-pLD50) of inorganic substances (three random splits) have shown that CORAL could be a good tool to model this endpoint. The average statistical characteristics for the CORAL models are the following: n=38, r2=0.8461, q2=0.8298, s=0.273, F=198 (subtraining set); n=37, r2=0.8144, s=0.322, F=154 (calibration set); and n=10, r2=0.8004, Rm (test)2 =0.7815, s=0.240, F=32 (validation set).

QSPR study for the prediction of half-wave potentials of benzoxazines by heuristic method and radial basis function neural network

The half-wave potential (E1/2) is an important electrochemical property of organic compounds. In this work, a quantitative structure-property relationship (QSPR) analysis has been conducted on the half-wave reduction potential (E1/2) of 40 substituted benzoxazines by means of both a heuristic method (HM) and a non-linear radial basis function neural network (RBFNN) modeling method. The statistical parameters provided by the HM model (R2 =0.946; F=152.576; RMSCV=0.0141) and the RBFNN model (R2=0.982; F=1034.171 and RMS =0.0209) indicated satisfactory stability and predictive ability. The obtained models showed that benzoxazines with larger Min valency of a S atom (MVSA), lower Relative number of H atom (RNHA) and Min n-n repulsion for a C-H bond (MnnRCHB) and Minimal Electrophilic Reactivity Index for a C atom (MERICA) can be more easily reduced. This QSPR approach can contribute to a better understanding of structural factors of the organic compounds that contribute to the E1/2, and can be useful in predicting the E1/2 of other compounds.