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.

A Quantitative Structure Property Relationship for Prediction of Flash Point of Alkanes Using Molecular Connectivity Indices

Many structure-property/activity studies use graph theoretical indices, which are based on the topological properties of a molecule viewed as a graph. Since topological indices can be derived directly from the molecular structure without any experimental effort, they provide a simple and straightforward method for property prediction. In this work the flash point of alkanes was modeled by a set of molecular connectivity indices （Х）, modified molecular connectivity indices （ ^mХ^v ） and valance molecular connectivity indices （ ^mХ^v ）, with ^mХ^v calculated using the hydrogen perturbation. A stepwise Multiple Linear Regression （MLR） method was used to select the best indices. The predicted flash points are in good agreement with the experimental data, with the average absolute deviation 4.3 K.

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.

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.

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.

QSAR of Estrogen of Bisphenol A with 3D Vector of Atomic Property Correlation

Considering atomic property vector and atomic correlative function, the 3-dimensional structural vector of atomic property correlation （3D-VAPC）, a novel descriptor,is defined to characterize a 3-dimensional molecular structure by introducing self-adaptability regulation mechanism and the idea of orientating to customers. Characterizing the structures of 25 bisphenol A compounds by this vector, the QSAR models of three kinds of estrogen activities （ER affinities, gene induction and cell proliferation） have high multiple correlation coefficient （Rcum^2=0.933, 0.813, 0.959） and cross verification coefficient （Qcum^2=0.847, 0.953, 0.798） by support vector machine （SVM）, which suits for nonlinear circumstances. The above results show that the models successfully express the correlation between structure and three kinds of estrogen activities. Therefore, 3D-VAPC exactly reflects the molecular structural information and SVM method correctly describes the correlation between information and property of the compounds.

A new quantitative structure-retention relationship model for predicting chromatographic retention time of oligonucleotides

An integrated approach is proposed to predict the chromatographic retention time of oligonucleotides based on quantitative structure-retention relationships （QSRR） models. First, the primary base sequences of oligonucleotides are translated into vectors based on scores of generalized base properties （SGBP）, involving physicochemical, quantum chemical, topological, spatial structural properties, etc.; thereafter, the sequence data are transformed into a uniform matrix by auto cross covariance （ACC）. ACC accounts for the interactions between bases at a certain distance apart in an oligonucleotide sequence; hence, this method adequately takes the neighboring effect into account. Then, a genetic algorithm is used to select the variables related to chromatographic retention behavior of oligonuclcotides. Finally, a support vector machine is used to develop QSRR models to predict chromatographic retention behavior. The whole dataset is divided into pairs of training sets and test sets with different proportions; as a result, it has been found that the QSRR models using more than 26 training samples have an appropriate external power, and can accurately represent the relationship between the features of sequences and structures, and the retention times. The results indicate that the SGBP-ACC approach is a useful structural representation method in QSRR of oligonucleotides due to its many advantages such as plentiful structural information, easy manipulation and high characterization competence. Moreover, the method can further be applied to predict chromatographic retention behavior of oligonucleotides.

人工智能辅助含能分子设计的应用与展望

含能分子研发面临多重挑战,传统“试错法”效率低下,计算机辅助分子设计的出现改变了研发模式。本综述回顾了含能分子设计的发展历程,介绍了计算机辅助含能分子设计的研究现状,并概述了人工智能技术(AI)在性质预测、分子生成、合成路线和反应条件预测等多个设计环节的最新进展,讨论了当前含能分子设计模式与其他材料设计方法的差距,思考差距产生的原因,并对未来AI辅助含能分子设计的发展方向提出展望。研究发现,AI在含能分子性能预测和分子生成等方面已经有了应用,但在合成路径规划和反应条件优化等环节的应用仍有待进一步探索,应用前景巨大。通过数据增强、迁移学习或高通量计算有望能够解决含能分子数据薄弱的问题;加强AI辅助含能分子合成路线与反应条件探索有望贯通“设计→评估→制备→验证”全流程自动化分子设计模式。AI辅助含能分子设计为提升含能分子设计水平提供新的可能性,有助提升含能分子研发效率。

基于定量构效关系模型的有机发光材料接触和吸入危害性评估

近期研究表明,部分有机发光材料(Organic Light-Emitting Materials,OLEMs)具有环境持久性和生物富集性。为明确OLEMs对人体健康的潜在危害,本研究通过专利检索,建立了1988种OLEMs的化学品清单,并基于化学品分类和标签数据库(Classification and Labelling Inventory,C&L清单)收录的94种OLEMs的人体健康危害分类与标签信息,构建定量构效关系(Quantitative Structure Property Relationship,QSPR)模型,应用欧氏距离、分子描述符标准化和核密度估计的方法定义模型应用域,对C&L清单未收录的1894种OLEMs的接触和吸入危害性进行预测。结果表明,1652种OLEMs可能通过接触对人体健康造成危害,1612种OLEMs可能通过吸入对人体健康造成危害。本研究为优先控制化学品识别和新化学物质环境管理提供了理论依据。

Heterogeneous Fenton degradation of azodyes catalyzed by modified polyacrylonitrile fiber Fe complexes: QSPR (quantitative structure peorperty relationship) study

The amidoximated polyacrylonitrile （PAN） fiber Fe complexeswere prepared and used as the heterogeneous Fenton catalysts for thedegradation of28 anionicwater soluble azodyes inwater under visible irradiation. The multiple linear regression （MLR） methodwas employed todevelop the quantitative structure property relationship （QSPR） model equations for thedecoloration and mineralization of azodyes. Moreover, the predictive ability of the QSPR model equationswas assessed using Leave-one-out （LOO） and cross-validation （CV） methods. Additionally, the effect of Fe content of catalyst and the sodium chloride inwater on QSPR model equationswere also investigated. The results indicated that the heterogeneous photo-Fentondegradation of the azodyeswithdifferent structureswas conducted in the presence of the amidoximated PAN fiber Fe complex. The QSPR model equations for thedyedecoloration and mineralizationwere successfullydeveloped using MLR technique. MW/S （molecularweightdivided by the number of sulphonate groups） and N N=N （the number of azo linkage） are considered as the most importantdetermining factor for thedyedegradation and mineralization, and there is a significant negative correlation between MW/S or N N=N anddegradation percentage or total organic carbon （TOC） removal. Moreover, LOO and CV analysis suggested that the obtained QSPR model equations have the better prediction ability. The variation in Fe content of catalyst and the addition of sodium chloridedid not alter the nature of the QSPR model equations.

基于原子类型电性拓扑状态指数的最小点火能预测模型研究

针对危险化学品最小点火能的试验测量存在一定的困难且具有很大不确定性的问题,收集了61种化学品的最小点火能试验值,并仅根据其分子结构计算了原子类型电性拓扑状态指数(ETSI),最终选择了23种ETSI作为分子描述符进行建模。首先,尝试基于61种危险化学品的最小点火能值,通过多元线性回归和支持向量机进行QSPR建模,但所建模型的内部稳定性很差,不满足基本的要求。经分析可能是因为描述符太多而样本相对较少造成的。通过从数据集中删除13个最小点火能值,ETSI的种类减少到14个,并采用相同的方法进行QSPR建模。经验证,新建模型在拟合能力、内部稳定性和外部预测能力3个方面均具有优异的性能,可以在不需要购买任何昂贵的软件和硬件的前提下,方便、快捷地预测化学品的最小点火能。

有机过氧化物自加速分解温度的神经网络研究

为了研究有机过氧化物自加速分解温度(SADT)与其结构之间的定量构效关系,计算了46种有机过氧化物的分子形状指数(nK)和电性距离矢量指数(M i).通过筛选优化,得到分子形状指数的2K、3K、4K及电性距离矢量指数的M 9、M 21、M 26、M 27、M 33,共8个结构参数,并将它们作为BP神经网络的输入层变量,有机过氧化物SADT作为输出层变量,采用8∶4∶1的网络结构,获得了令人满意的定量结构与性质关系(QSPR)神经网络预测模型,其总相关系数为0.997,自加速分解温度的预测值与实验值的一致性令人满意,平均误差仅为0.9℃,优于文献方法.结果表明,有机过氧化物自加速分解温度与8种结构参数之间呈现良好的非线性关系.

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。

一个新的分子拓扑指数

通过将键参数和量子数引入原子点价,重新定义原子点价δYi,认为分子中某一原子的δYi与该原子的杨氏电负性力标、价电子数、成键电子数及最外层主量子数有关.由此提出新的分子拓扑指数mXY.并用一级指数1XY与饱和烷烃的沸点、液体热容、气体热容、蒸发热、临界温度、临界压力,卤代苯的辛醇/水分配系数,烷氧氯硅烷的气相色谱保留指数、含氮杂环化合物的毒性,碱金属卤化物的晶格焓与F心能带,卤化锡的119SnMossbauer等性质/活性进行相关关系的研究.结果表明,1XY与有机物和无机物的性质/活性间具有广泛良好的相关性.

孔隙结构对碎屑储集岩物性控制作用的定量描述

油气储集岩的微观孔隙结构对其物性具有重要的控制作用.通过扫描电镜的直接观察和对应的孔渗物性分析揭示了微观孔隙结构与岩石孔渗性能呈良好的正相关关系.由常规压汞法可以提取多项表征储集层微观孔隙结构的特征参数,使用对应分析数学地质手段筛选出主控因素.通过多元线性回归,获得了储集岩孔隙度、渗透率与其孔隙结构参数之间的定量关系,以及储集岩孔隙度与其孔隙结构参数及矿物组成之间的定量关系.这些定量关系证明了孔隙结构对碎屑储集岩物性的明显控制作用,为储集岩孔渗性能的预测提供了新的手段.

钛合金组织特征参数与拉伸性能预测

研究了TA15钛合金β区热变形时,变形温度、变形量、变形速率、冷却方式等工艺参量对片状组织特征参数β晶粒尺寸D、α集束尺寸d、α片厚b的影响,以及片状组织特征参数与力学性能的关系。利用多元回归分析得到了强度、塑性与3个特征参数D、d、b的定量关系式,通过这种定量关系可以预测力学性能。

一个新的分子电性距离矢量(MEDV)

有机化合物通常由电性各异的碳、氢、氧、氮、硫等元素以共价键相互连接而成.我们提出一个称为分子电性距离矢量(简称MEDV)的新描述子来表征有机分子结构.以含有环、饱和与不饱和键及碳氧氮等元素的色氨酸分子为例,说明构建MEDV的基本方法.开发设计了计算有机分子MEDV的True BASIC语言程序.不借用其他任何描述子,仅用MEDV建立了烷、醇、多环芳烃等多种有机化合物包括沸点、密度、保留指数等多种性质的定量结构-性质相关(QSPR)模型,相关系数均达0.98以上.