Estimation of surface tension of organic compounds using quantitative structure-property relationship

A novel quantitative structure-property relationship （QSPR） model for estimating the solution surface tension of 92 organic compounds at 20℃ was developed based on newly introduced atom-type topological indices. The data set contained non-polar and polar liquids, and saturated and unsaturated compounds. The regression analysis shows that excellent result is obtained with multiple linear regression. The predictive power of the proposed model was discussed using the leave-one-out （LOO） cross-validated （CV） method. The correlation coefficient （R） and the leave-one-out cross-validation correlation coefficient （Rcv） of multiple linear regression model are 0.991 4 and 0.991 3, respectively. The new model gives the average absolute relative deviation of 1.81% for 92 substances. The result demonstrates that novel topological indices based on the equilibrium electro-negativity of atom and the relative bond length are useful model parameters for QSPR analysis of compounds.

Quantitative Structure-Property Relationship on Prediction of Surface Tension of Nonionic Surfactants

A quantitative structure-property relationship (QSPR) study has been made for the prediction of the surface tension of nonionic surfactants in aqueous solution. The regressed model includes a topological descriptor, the Kier & Hall index of zero order (KH0) of the hydrophobic segment of surfactant and a quantum chemical one, the heat of formation (fHD) of surfactant molecules. The established general QSPR between the surface tension and the descriptors produces a correlation coefficient of multiple determination, 2r=0.9877, for 30 studied nonionic surfactants.

Solubility study of hydrogen in direct coal liquefaction solvent based on quantitative structure–property relationships model

Direct coal liquefaction(DCL)is an important and effective method of converting coal into high-valueadded chemicals and fuel oil.In DCL,heating the direct coal liquefaction solvent(DCLS)from low to high temperature and pre-hydrogenation of the DCLS are critical steps.Therefore,studying the dissolution of hydrogen in DCLS under liquefaction conditions gains importance.However,it is difficult to precisely determine hydrogen solubility only by experiments,especially under the actual DCL conditions.To address this issue,we developed a prediction model of hydrogen solubility in a single solvent based on the machine-learning quantitative structure–property relationship(ML-QSPR)methods.The results showed that the squared correlation coefficient R^(2)=0.92 and root mean square error RMSE=0.095,indicating the model’s good statistical performance.The external validation of the model also reveals excellent accuracy and predictive ability.Molecular polarization(a)is the main factor affecting the dissolution of hydrogen in DCLS.The hydrogen solubility in acyclic alkanes increases with increasing carbon number.Whereas in polycyclic aromatics,it decreases with increasing ring number,and in hydrogenated aromatics,it increases with hydrogenation degree.This work provides a new reference for the selection and proportioning of DCLS,i.e.,a solvent with higher hydrogen solubility can be added to provide active hydrogen for the reaction and thus reduce the hydrogen pressure.Besides,it brings important insight into the theoretical significance and practical value of the DCL.

Quantitative Correlation of Chromatographic Retention and Acute Toxicity for Alkyl(1-phenylsulfonyl) Cycloalkane Carboxylates and Their Structural Parameters by DFT

Twenty eight alkyl(1-phenylsulfonyl) cycloalkane carboxylates were computed at the B3LYP/6-31G* level. Based on linear solvation energy theory, two quantitative correlation equations of the molecular structures of alkyl(1-phenylsulfonyl) cycloalkane carboxylate com- pounds to their chromatographic retention (capacity factor lgKW) and the toxicity for photo- bacterium phosphoreum (–lgEC50) were developed by using the molecular structural parameters as theoretical descriptors (r2 = 0.9501, 0.9488). The two quantitative correlation equations were consequently cross validated by leave-one-out (LOO) validation method with q2 of 0.9113 and 0.9281, respectively. The result showed that the two equations achieved in this work by B3LYP/6-31G* are both more advantageous than those from AM1, and can be used to predict the lgKW and –lgEC50 of congeneric organics.

Quantitative Models for the Structure and Photodegradation of Polycyclic Aromatic Hydrocarbons

Based on the quantum chemical descriptors,quantitative structure-property relationship（QSPR） models have been developed to estimate and predict the photodegradation rate constant（logK） of polycyclic aromatic hydrocarbons（PAHs） by use of linear method（multiple linear regression,MLR） and non-linear method（back propagation artificial neural network,BP-ANN）.A BP-ANN with 3-3-1 architecture was generated by using three quantum chemical descriptors appearing in the MLR model.The standard heat of formation（HOF）,the gap of frontier molecular orbital energies（ΔELH） and total energy（TE） were inputs and its output was logK.Leave-One-Out（LOO） Cross-Validated correlation coefficient（R^2CV） of the established MLR and BP-ANN models were 0.6383 and 0.7843,respectively.The nonlinear BP-ANN model has better predictive ability compared to the linear MLR model with the root mean square error（RMSE） for training and validation sets to be 0.1071,0.1514 and the squared correlation coefficient（R^2） of 0.9791,0.9897,respectively.In addition,some insights into the molecular structural features affecting the photodegradation of PAHs were also discussed.

Molecular Structural Characterization and Quantitative Prediction of Reduced Ion Mobility Constants for Diversified Organic Compounds

Based on two-dimensional topological structures, a novel molecular electronegativity interaction vector with hybridization （MEHIV） was developed to describe atomic hybridization state in different molecular environments. Five quantitative models by MEHIV characterization and multiple linear regression modeling were successfully established to predict reduced ion mobility constants （Ko） of alkanes, aromatic hydrocarbons, fatty alcohols, fatty aldehydes and ketones and carboxylic esters. The correlation coefficients Roy by leave-one-out cross-validation are 0.792, 0.787, 0,949, 0.972 and 0.981, respectively, and the standard deviations SDcv are 0.067, 0.086, 0.064, 0.043 and 0.042, respectively. These results suggested that MEHIV is an excellent topological index descriptor with many advantages such as straightforward physicochemical meaning, high characterization competence, convenient expansibility and easy manipulation.

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.

A computational toolbox for molecular property prediction based on quantum mechanics and quantitative structure-property relationship

Chemical industry is always seeking opportunities to efficiently and economically convert raw materials to commodity chemicals and higher value-added chemicalbased products.The life cycles of chemical products involve the procedures of conceptual product designs,experimental investigations,sustainable manufactures through appropriate chemical processes and waste disposals.During these periods,one of the most important keys is the molecular property prediction models associating molecular structures with product properties.In this paper,a framework combining quantum mechanics and quantitative structure-property relationship is established for fast molecular property predictions,such as activity coefficient,and so forth.The workflow of framework consists of three steps.In the first step,a database is created for collections of basic molecular information;in the second step,quantum mechanics-based calculations are performed to predict quantum mechanics-based/derived molecular properties(pseudo experimental data),which are stored in a database and further provided for the developments of quantitative structure-property relationship methods for fast predictions of properties in the third step.The whole framework has been carried out within a molecular property prediction toolbox.Two case studies highlighting different aspects of the toolbox involving the predictions of heats of reaction and solid-liquid phase equilibriums are presented.

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.

Estimation of photolysis half-lives of dyes in a continuous-flow system with the aid of quantitative structure-property relationship

In this paper the photolysis half-lives of the model dyes in water solutions and under ultraviolet （UV） radiation were determined by using a continuous-flow spectrophotometric method. A quantitative structure- property relationship （QSPR） study was carried out using 21 descriptors based on different chemometric tools including stepwise multiple linear regression （MLR） and partial least squares （PLS） for the prediction of the photolysis half-life （t1/2） of dyes. For the selection of test set compounds, a K-means clustering technique was used to classify the entire data set, so that all clusters were properly represented in both training and test sets. The QSPR results obtained with these models show that in MLR-derived model, photolysis half-lives of dyes depended strongly on energy of the highest occupied molecular orbital （EHoMO）, largest electron density of an atom in the molecule （ED^＋） and lipophilicity （logP）. While in the model derived from PLS, besides aforementioned EHOMO and ED^＋ descriptors, the molecular surface area （Sm）, molecular weight （M-W）, electronegativity （X）, energy of the second highest occupied molecular orbital （EHoMO- 1） and dipole moment （μ） had dominant effects on logt1/2 values of dyes. These were applicable for all classes of studied dyes （including monoazo, disazo, oxazine, sulfo- nephthaleins and derivatives of fluorescein）. The results were also assessed for their consistency with findings from other similar studies.

Prediction on Critical Micelle Concentration of Nonionic Surfactants in Aqueous Solution:Quantitative Structure-Property Relationship Approach

In order to predict the critical micelle concentration (cmc) of nonionic surfactants in aqueous solution,a quantitative structure-property relationship (QSPR) was found for 77 nonionic surfactants belonging to eight series. The best-regressed model contained four quantum-chemical descriptors,the heat of formation (ΔH),the molecular dipole moment (D),the energy of the lowest unoccupied molecular orbital (E_ LUMO ) and the energy of the highest occupied molecular orbital (E_ HOMO ) of the surfactant molecule; two constitutional descriptors,the molecular weight of surfactant (M) and the number of oxygen and nitrogen atoms (n_ ON ) of the hydrophilic fragment of surfactant molecule; and one topological descriptor,the Kier & Hall index of zero order (KH0) of the hydrophobic fragment of the surfactant. The established general QSPR between lg(cmc) and the descriptors produced a relevant coefficient of multiple determination:R 2=0.986. When cross terms were considered,the corresponding best model contained five descriptors E_ LUMO ,D,KH0,M and a cross term n_ ON ·KH0,which also produced the same coefficient as the seven-parameter model.

屏蔽酚分子结构与高温抗氧化性能关系研究

分别采用热失重法和加压差示扫描量热法考察了不同结构屏蔽酚的热稳定性及其高温抗氧化性能,采用基于密度泛函理论的分子模拟方法计算了屏蔽酚分子的抗氧化特征参数,运用遗传算法建立了屏蔽酚分子结构参数与高温氧化诱导期之间的定量关系方程。结果表明,相对分子质量是影响屏蔽酚热稳定性的关键因素;所建立的定量结构与性能关系方程具有明确的物理化学意义,交叉检验相关系数R CV^(2)为0.914,具有较好的预测能力;揭示出S原子Mulliken负电荷数、O—H键解离能和分子最高占据轨道能量是影响屏蔽酚高温氧化性能的关键分子结构特征参数,且三个参数影响高温抗氧化性能的权重大小依次降低。从改善抗氧化性能的角度出发,应该设计开发具备如下结构特征的含硫屏蔽酚:S原子Mulliken负电荷较多、O—H键解离能较低、分子最高占据轨道能量较高。这为新结构高性能屏蔽酚类抗氧剂产品的设计开发指明了方向。

原子电性作用矢量和杂化状态指数用于氨基酸核磁共振碳谱模拟

提出了用于表征分子局部化学微环境及原子所处杂化状态的结构描述子:原子电性作用矢量(AEIV)和原子杂化状态指数(AHSI),将其应用于20个天然氨基酸103个碳原子13C核磁共振模拟中,取得满意结果。模型计算值、留一法(LOO-CV)交互校验预测值和新颖的留一分子法(LMO)交互校验预测值的复相关系数分别为r=0.9948、0.9940和0.9924。进一步使用4个非天然氨基酸化学位移值来测试该模型的预测能力,预测复相关系数为r=0.9940。

平纵组合线形几何特征对车速变化的影响

利用同济大学高仿真驾驶模拟器,采集山区高速公路连续车速数据,将车速变化划分为减速、稳定车速及提速3个区间.通过多项Logistic回归模型建立组合线形及相邻路段几何特征与车速变化的定量关系.结果表明:组合线形中,坡长越长,平均坡度越大,越不易维持稳定车速;下坡时,提速通过平纵组合线形路段的可能性大;向左转弯,维持稳定车速的可能性大;上游和平纵组合线形的坡度差越大,越不易维持稳定车速;下游路段的曲率越大,减速通过平纵组合线形路段的可能性越大.

原子电性作用矢量和杂化状态指数用于酮类化合物核磁共振QSSR研究

从分子二维结构出发,采用反映分子局部化学微环境结构表述指数———原子电性相互作用矢量(AEIV)和原子杂化状态指数(AHSI)对201个酮分子中的1 373个碳原子的13CNMR谱建模模拟,取得了满意结果。所得定量结构波谱关系(QSSR)的多元线性回归模型复相关系数RMM及留一法交互检验相关系数RCV分别为0.966和0.965。进一步随机抽出5个化合物中共计196个13CNMR化学位移用于模型外部验证,预测结果复相关系数Rpred=0.965,上述结果表明所建模型有良好的稳定性和泛化能力。

有机磷酸酯类化合物定量结构-色谱保留关系及稳健性分析

以分子拓扑指数作为结构描述符,采用最佳子集回归方法,建立了35种有机磷酸酯类化合物在3种不同极性固定相上的定量结构-色谱保留关系(QSRR)模型。3个QSRR模型均具有良好的拟合能力,对QSRR模型分别进行了交叉验证及外部数据集验证,结果表明各模型具有较强的预测能力。对QSRR模型的系数、标准误差及相关系数均进行了蒙特卡洛模拟,结果证实蒙特卡洛方法可用于QSRR模型的稳健性分析。

阳离子捕收剂及其分子结构设计理论研究进展

阳离子捕收剂在硅酸盐、碳酸盐、钾盐、金属氧化矿等矿物的分选中表现出巨大的应用潜力,因此,新型阳离子捕收剂的研发一直是浮选药剂领域的研究热点。综述了国内外阳离子捕收剂的研究进展,与国外相比,我国阳离子浮选研究的起步较晚,尽管近年来国内已开发出多种新型的阳离子捕收剂,但阳离子捕收剂设计研发及工业化应用缓慢,仍制约着阳离子浮选工艺的推广。概述了计算机辅助浮选药剂分子结构设计的发展,详细介绍了浮选药剂研究过程中常用的3种计算机辅助技术方法:密度泛函数理论、定量构效关系和分子动力学模拟;综述了分子动力学模拟技术在浮选药剂研发中的应用。在此基础上,分析了目前浮选药剂分子结构设计方法的不足,结合密度泛函数理论和分子动力学模拟技术,介绍了一种基于能量判据的高效浮选药剂设计方法,探讨了该方法在阳离子捕收剂分子结构设计中的应用前景,提出并阐述了提高该方法适用性的建议,为新型高效阳离子捕收剂的针对性设计提供参考。

苯的硝基和叠氮基衍生物热力学性质的构效关系

苯的硝基和叠氮基衍生物是一类重要的含能材料,为了揭示其热力学性质与分子结构之间的关系,采用第一性原理进行了计算研究.通过计算平衡电负性连接指数,结合分子结构描述符,对苯的硝基和叠氮基衍生物的热力学性质建立了构效关系模型.模型检验结果表明,构建的模型具有良好的稳健性和预测能力,所得模型为苯的硝基和叠氮基衍生物的爆轰参数计算和分解机理研究提供了一种快速的热力学性质预测方法 .

Explanatory System of Support Vector Regression and Its Application in QSPR of Surfactants

In order to solve the problem of poor interpretability of support vector re- gression （SVR） applied in quantitative structure-property relationship （QSPR）, a com- plete set of explanatory system for SVR was established based on F-test, The nov- el explanatory system includes significance tests of model and single-descriptor im- portance, single-descriptor effect and sensitivity analysis, and significance tests of interaction between two descriptors, etc. The results of example indicated that the explanatory results of the new system were consistent well with those of stepwise linear regression model and quadratic polynomial stepwise regression model. The explanatory SVR model will play an important role in regression analysis such as QSPR.

气藏递减期生产动态预测定量关系模型

稳产期结束后,气藏开发进入产量递减期。为快速、准确预测气藏生产动态规律,基于气藏采气速度与稳产期定量关系研究成果,结合定容气藏物质平衡方程和气井二项式产能方程,应用气藏工程原理,建立正常压力气藏递减期生产动态预测定量关系模型,得出了气藏递减期采气速度与生产时间定量关系式。以某气井为计算实例,将文中方法与数值模拟方法对比。计算结果表明:该模型能准确预测任意稳产期采气速度下产量递减期的生产动态。同时,从该模型中能得出部分影响气藏产量递减快慢的因素。