Prediction of liquid chromatography retention factors for α-branched phenylsulfonyl acetates using quantum chemical descriptors

The logarithms of retention factors normalized to a hypothetical pure water eluent(log k w) were determined on a reversed phase high performance liquid chromatography(RP HPLC) column (Li Chrosorb RP 18 column) for 20 new α\|branched phenylsulfonyl acetates. The atomic charge method was applied to develop quantitative structure retention relationships(QSRRs). Among the available geometric and electronic descriptors, surface area (S), ovality (O), and the charge of carboxyl group(Q OC ) are significant. In the model, the contribution of surface area (S) is the greatest. The molecular mechanism of retention was demonstrated through the model. With the correlation coefficient ( r 2 adj , adjusted for degrees of freedom) of 0.964, the standard error of 0.164 and the F value of 170.39, the model has good predictive capacity.

Application of quantum chemical descriptors into quantitative structure-property relationship models for prediction of the photolysis half-life of PCBs in water

Quantitative structure-property relationship(QSPR)models were developed for prediction of photolysis half-life(t_(1/2))of polychlorinated biphenyls(PCBs)in water under ultraviolet(UV)radiation.Quantum chemical descriptors computed by the PM3 Hamiltonian software were used as independent variables.The cross-validated Q^(2)_(cum)value for the optimal QSPR model is 0.966,indicating good prediction capability for lg t_(1/2)values of PCBs in water.The QSPR results show that the largest negative atomic charge on a carbon atom(Q-C)and the standard heat of formation(ΔH_(f))have a dominant effect on t_(1/2)values of PCBs.Higher Q_(C)^(-)values or lowerΔHf values of the PCBs leads to higher lg t_(1/2)values.In addition,the lg t_(1/2)values of PCBs increase with the increase in the energy of the highest occupied molecular orbital values.Increasing the largest positive atomic charge on a chlorine atom and the most positive net atomic charge on a hydrogen atom in PCBs leads to the decrease of lg t_(1/2)values.

Descriptors-based machine-learning prediction of cetane number using quantitative structure–property relationship

The physicochemical properties of liquid alternative fuels are important but difficult to measure/predict, especially when complex surrogate fuels are concerned. In the present work, machine learning is used to develop quantitative structure–property relationship models. The fuel chemical structure is represented by molecular descriptors, allowing the linking of important features of the fuel composition and key properties of fuel utilization. Feature selection is employed to select the most relevant features that describe the chemical structure of the fuel and several machine learning algorithms are tested to construct interpretable models. The effectiveness of the methodology is demonstrated through the development of accurate and interpretable predictive models for cetane numbers, with a focus on understanding the link between molecular structure and fuel properties. In this context, matrix-based descriptors and descriptors related to the number of atoms in the molecule are directly linked with the cetane number of hydrocarbons. Furthermore, the results showed that molecular connectivity indices play a role in the cetane number for aromatic molecules. Also, the methodology is extended to predict the cetane number of ester and ether molecules, leveraging the design of alternative fuels towards fully sustainable fuel utilization.

Photolysis of mono- through deca-chlorinated biphenyls by ultraviolet irradiation in n-hexane and quantitative structure-property relationship analysis

The photolysis of 16 polychlorinated biphenyls （PCBs） （including mono- through deca-chlorinated） in n-hexane was investigated under ultraviolet irradiation using a 500-W high-pressure mercury lamp. Photolysis of PCBs follows pseudo-first-order reaction kinetics, with photolysis rate constants ranging between 0.0011 s^-1 for PCB-52 and 0.0574 s^-1 for PCB-118. The degradation rates of PCBs by high-pressure mercury lamp irradiation were remarkably independent with respect to the degree of chlorination. Furthermore, partial least squares （PLS） models were developed to provide insight into which aspect of the molecular structure influenced PCB photolysis rate constants. It was found that the photolysis rates of PCBs increased with an increase in the net charge on the carbon atom （qc）, （ELUMO-EHOMO）^2, and the Y-axis dipole moment （μy） values, or the decrease in the energy of the second highest occupied molecular orbital （EHOMO-1）, energy of the lowest unoccupied molecular orbital （ELUMO）, ELUMO ＋ EHOMO, ELUMO - EHOMO, most positive atomic charge （q^＋）, and the twist angle of the chlorine atom （TA） values.

Quantitative structure-property relationship of aromatic sulfur-containing carboxylates

Based on quantum chemical calculations, TLSER model(theoretical linear solvation energy relationships) and atomic charge approach were applied to model the partition properties(water solubility and octanol/water partition coefficient) of 96 aromatic sulfur-containing carboxylates, including phenylthio, phenylsulfinyl and phenylsulfonyl carboxylates. In comparison with TLSER models, the atomic charge models are more accurate and reliable to predict the partition properties of the kind of compounds. For the atomic charge models, the molecular descriptors are molecular surface area(S), molecular shape(O), weight(M W), net charges on carboxyl group(Q OC), net charges of nitrogen atoms(Q N), and the most negative atomic charge(q -) of the solute molecule. For water solubility(log S W) and octanol/water partition coefficient(log K OW), the correction coefficients r 2 adj(adjusted for degrees of freedom) are 0.936 and 0.938, and the standard deviations are 0.364 and 0.223, respectively.

Predicting octanol/water partition coefficient using solvation free energy and solvent-accessible surface area

The regression model for octanol/water partition coefficients ( K ow ), is founded with only two molecular descriptors available through quantum chemical calculations: solvation free energy (Δ G S ), and solvent accessible surface area (SASA). For the properties of 47 organic compounds from 17 types, the model gives a correction coefficient (adjusted for degrees of freedom) of 0 959 and a standard error of 0 277 log unit. It is a suitable way to predict the partition properties that are related to solute solvent interactions in the water phase.

Developing the QSPR model for predicting the storage lipid/water distribution coefficient of organic compounds

The distribution of organic compounds in stored lipids affects their migration,transformation,bioaccumulation,and toxicity in organisms.The storage lipid/water distribution coefficient(log K_(lip/w))of organic chemicals,which quantitatively determines such distribution,has become a key parameter to assist their ecological security and health risk.Due to the impossibility to measure K_(lip/w)values for a huge amount of chemicals,it is necessary to develop predictive approaches.In this work,a quantitative structure-property relationship(QSPR)model for estimating log K_(lip/w)values of small organic compounds was constructed based on 305 experimental log K_(lip/w)values.Quantum chemical descriptors and n-octanol/water partitioning coefficient were employed to characterize the intermolecular interactions that dominate log K_(lip/w)values.The hydrophobic and electrostatic interactions and molecular size have been found to play important roles in governing the distribution of chemicals between lipids and aqueous phases.The regression(R2=0.959)and validation(Q2=0.960)results indicate good fitting performance and robustness of the developed model.A comparison with the predictive performance of other commercial software further proves the higher accuracy and stronger predictive ability of the developed K_(lip/w)predictive model.Thus,it can be used to predict the K_(lip/w)values of cycloalkanes,long-chain alkanes,halides(with fluorine,chlorine,and bromine as substituents),esters(without phosphate groups),alcohols(without methoxy groups),and aromatic compounds.

Multi-objective Modeling and Assessment of Partition Properties: A GA-Based Quantitative Structure-Property Relationship Approach

In this work a multi-objective quantitative structure-property relationship (QSPR) analysis approach was reported based on the study on three partition properties of 50 aromatic sulfur-containing carboxylates. Here multi-objectives (properties) were taken as a vector for QSPR modeling. The quantitative correlations for partition properties were developed using a genetic algorithm-based variable-selection approach with quantum chemical descriptors derived from AM1-based calculations. With the QSPR models, the aqueous solubility, octanol/water partition coefficients and reversed-phase HPLC capacity factors of sulfur-containing compounds were estimated and predicted. Using GA-based multivariate linear regression with cross-validation procedure, a set of the most promising descriptors was selected from a pool of 28 quantum chemical semi-empirical descriptors, including steric and electronic types, to integrally build QSPR models. The selected molecular descriptors included the net charges on carboxyl group (Q OC), the 2nd power of net charges on nitrogen atoms (Q 2 N), the net atomic charge on the sulfur atoms (Q S), the van der Waals volume of molecule (V), the most positive net atomic charge on hydrogen atoms (Q H) and the measure of polarity and polarizability (π), which were main factors affecting the distribution processes of the compounds under study. The statistically best QSPR models of six descriptors were simultaneously obtained by GA-based linear regression analysis. With the selected descriptors and the QSPR equations, mechanisms of partition action of the Sulfur-containing carboxylates were able to be investigated and interpreted.