Prediction of normal melting point of pure substances by a reference series method

The “Reference Series” method of Shacham et al. is modified to enable prediction of normal melting point temperatures (T_m) of pure substances within experimental error level. A homologous series for which large amount and high precision T_m data are available is used as a “reference” series. To predict T_m for a “target” series quantitative property‐property relationships (QPPRs) are derived to represent the predicted T_m values of the “target” series in terms of the T_m values of the reference series. Two QPPRs are necessary in order to match the odd and even carbon number (n_C) oscillations of T_m in the low n_C region. In the high n_C region, the QPPR is adjusted to represent correctly the asymptotic behavior of T_m. It is shown that the method is very useful for consistency analysis of T_m data and enables a reliable prediction of T_m in both the low n_C and the high n_C regions. © 2013 American Institute of Chemical Engineers AIChE J, 59: 3730–3740, 2013     

Prediction of Enthalpy of Fusion of Non‐aromatic Energetic Compounds Containing Nitramine,Nitrate and Nitro Functional Groups

A new and reliable approach is introduced to predict enthalpy of fusion of non‐aromatic energetic compounds, which contain nitramine, nitrate and nitro functional groups. It is useful in diminishing hazardous synthesis and testing of energetic compounds. Simple relationship for predicting enthalpy of fusion can be expressed as a combination of the number of oxygen atoms and three structural parameters. Some experimental data of non‐aromatic energetic compounds were used to obtain the new correlation. The presented method has been tested with different non‐aromatic energetic compounds with complex molecular structures. The model can offer reliable predictions of enthalpy of fusion for non‐aromatic energetic compounds in comparison to the best available predictive methods. The enthalpy of fusion for some new non‐aromatic explosives such as CL‐20 and TNAZ was predicted.     

以性质为基础的同时设计过程和分子的方法

In this work, property clustering techniques and group contribution methods are combined to enable simultaneous consideration of process performance requirements and molecular property constraints. Using this methodology, the process design problem is solved to identify the property targets corresponding to the desired process performance. A significant advantage of the developed methodology is that for problems that can be satisfactorily described by only three properties, the process and molecular design problems can be simultaneously solved visually on a ternary diagram, irrespective of how many molecular fragments are included in the search space. On the ternary cluster diagram, the target properties are represented as individual points if given as discrete values or as a region if given as intervals. The structure and identity of candidate components is then identified by combining or ＂mixing＂ molecular fragments until the resulting properties match the targets.     

Two-layer model predictive control for chemical process model with integrating controlled variables

This paper proposes an overall solution to the two-layer model predictive control (MPC) for the integrating controlled variables in the process model. The scheme includes three modules, that is, the open-loop prediction module, the steady-state target calculation (SSTC) module, and the dynamic control module. Based on the real-time output measurements and past inputs, the open-loop prediction module predicts the future outputs in the presence of disturbances. The economic optimization of SSTC is comprised of the feasibility stage and the economics stage, considering constraints of multi-priority ranks. The dynamic control module receives the steady-state targets from SSTC and calculates the control signals. The optimization problems of SSTC and dynamic control operate with the same frequency. This overall method guarantees the consistency of three modules with respect to the model, the constraints, and the targets. The simulation example illustrates that steady-state targets are adjusted dynamically after the occurrence of disturbances, and offset-free control is achieved.     

TRANSPORT PROPERTY DATA QUALIFICATION FOR VARYING FUNCTIONAL GROUPS

A qualitative study of the accuracy and consistency of transport property experimental data was undertaken for compounds which are not categorized in particular homologous series but contain a common hydrocarbon chain and differing functional groups. The behaviors of liquid and vapor viscosity and vapor thermal conductivity were observed over a reasonable temperature range. Plots of property against temperature or reduced temperature were analyzed for consistent alignment among different properties and groups of compounds. Trends were similar to past work on thermodynamic properties. Results of the study provide methods to verify data accuracy, predict positioning of data within functional group categories, and allow rough prediction for multifunctional compounds.     

Promiscuity and the Conformational Rearrangement of Drug‐Like Molecules: Insight from the Protein Data Bank

Selectivity is a central aspect of lead optimization in the drug discovery process. Medicinal chemists often try to decrease molecular flexibility to improve selectivity, given the common belief that the two are interdependent. To investigate the relationship between polypharmacology and conformational flexibility, we mined the Protein Data Bank and constructed a dataset of pharmaceutically relevant ligands that crystallized in more than one protein target while binding to each co‐crystallized receptor with similar in vitro affinities. After analyzing the molecular conformations of these 100 ligands, we found that 59 ligands bound to different protein targets without significantly changing conformation, suggesting that there is no distinct correlation between conformational flexibility and polypharmacology within our dataset. Ligands crystallized in similar proteins and highly ligand‐efficient compounds with five or fewer rotatable bonds were less likely to adjust conformation when binding.     

Prediction of the flash points of alkanes by group bond contribution method using artificial neural networks

A group bond contribution model using artificial neural networks, which had the high ability of nonlinear of prediction, was established to predict the flash points of alkanes. This model contained not only the information of group property but also connectivity in molecules. A set of 16 group bonds were used as input parameters of neural networks to study the correlation of molecular structures with flash points of 44 alkanes. The results showed that the predicted flash points were in good agreement with the experimental data that the absolute mean absolute error was 6.9 K and the absolute mean relative error was 2.29%, which were superior to those of traditional group contribution methods. The method can be used not only to reveal the quantitative correlation between flash points and molecular structures of alkanes but also to predict the flash points of organic compounds for chemical engineering. __________ Translated from Chemical Engineering (China), 2007, 35(4): 38–41 [译自: 化学工程]     

人工神经网络基团键贡献法预测烷烃闪点

建立了一个基于人工神经网络方法的基团键贡献模型,用于预测烷烃闪点。该模型既考虑了分子中基团的特性,又考虑了基团之间的连接性(化学键)。以16种烷烃基团键作为神经网络的输入参数,研究了44种烷烃的闪点与分子结构之间的相关性。结果表明,闪点预测值与实验值符合良好,绝对平均绝对误差6.0 K,绝对平均相对误差2.15%,优于传统基团贡献法所得结果。该方法的提出不仅揭示了烷烃闪点与分子结构之间的定量关系,而且为工程上提供了一种预测有机物闪点的新的有效方法。     

Improved Artificial Neural Network for Data Analysis and Property Prediction in Slag Glass–Ceramic

The development of slag glass–ceramics has environmental and commercial value. However, new types of these materials are usually developed using the "trial and error" method because of little understanding of the relationship between the composition, processing, microstructure, and properties. In this paper, artificial neural network (ANN) technology was applied to investigate the relationship between the composition content and the properties of slag glass–ceramic. The investigation showed that the ANN model had an outstanding learning ability and was effective in complex data analysis. If the data set reflects the relationship of the composition and property, the trained network will learn the relationship and then give relatively accurate and stable prediction. A new "virtual sample" technology has also been created which improves the prediction performance of the network by providing greater accuracy and reliability. With this virtual sample technology, the ANN model can establish the exact relationship from a small-size-data set, and gives accurate predictions. This improved ANN model is a powerful and reliable tool for data analysis and property prediction, and will facilitate the material design and development of slag glass–ceramics.     

基于SVR误差补偿技术的神经网络城市污水厂水质预测

城市污水处理是一个复杂的生化处理过程,现在的神经网络技术无法做到对此过程精准建模.为了解决该问题,提出了一种基于SVR误差补偿技术的神经网络水质预测算法.该算法先利用BP神经网络对水质处理过程进行映射,再利用SVR误差补偿模型获得BP网络的预测补偿,进行预测数据校正.为了验证补偿模型的性能,还组织了马尔科夫补偿模型的对...     

Computer-Aided and AILDE Approaches to Design Novel 4-Hydroxyphenylpyruvate Dioxygenase Inhibitors

4-Hydroxyphenylpyruvate dioxygenase (HPPD) is a pivotal enzyme in tocopherol and plastoquinone synthesis and a potential target for novel herbicides. Thirty-five pyridine derivatives were selected to establish a Topomer comparative molecular field analysis (Topomer CoMFA) model to obtain correlation information between HPPD inhibitory activity and the molecular structure. A credible and predictive Topomer CoMFA model was established by “split in two R-groups” cutting methods and fragment combinations (q² = 0.703, r² = 0.957, ONC = 6). The established model was used to screen out more active compounds and was optimized through the auto in silico ligand directing evolution (AILDE) platform to obtain potential HPPD inhibitors. Twenty-two new compounds with theoretically good HPPD inhibition were obtained by combining the high-activity contribution substituents in the existing molecules with the R-group search via Topomer search. Molecular docking results revealed that most of the 22 fresh compounds could form stable π-π interactions. The absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction and drug-like properties made 9 compounds potential HPPD inhibitors. Molecular dynamics simulation indicated that Compounds Y12 and Y14 showed good root mean square deviation (RMSD) and root mean square fluctuation (RMSF) values and stability. According to the AILDE online verification, 5 new compounds with potential HPPD inhibition were discovered as HPPD inhibitor candidates. This study provides beneficial insights for subsequent HPPD inhibitor design.     

Methods and Applications of In Silico Aptamer Design and Modeling

Aptamers are nucleic acid analogues of antibodies with high affinity to different targets, such as cells, viruses, proteins, inorganic materials, and coenzymes. Empirical approaches allow the design of in vitro aptamers that bind particularly to a target molecule with high affinity and selectivity. Theoretical methods allow significant expansion of the possibilities of aptamer design. In this study, we review theoretical and joint theoretical-experimental studies dedicated to aptamer design and modeling. We consider aptamers with different targets, such as proteins, antibiotics, organophosphates, nucleobases, amino acids, and drugs. During nucleic acid modeling and in silico design, a full set of in silico methods can be applied, such as docking, molecular dynamics (MD), and statistical analysis. The typical modeling workflow starts with structure prediction. Then, docking of target and aptamer is performed. Next, MD simulations are performed, which allows for an evaluation of the stability of aptamer/ligand complexes and determination of the binding energies with higher accuracy. Then, aptamer/ligand interactions are analyzed, and mutations of studied aptamers made. Subsequently, the whole procedure of molecular modeling can be reiterated. Thus, the interactions between aptamers and their ligands are complex and difficult to understand using only experimental approaches. Docking and MD are irreplaceable when aptamers are studied in silico.     

以离子液体密度为例的分子性质预测模型建模方法探讨

分子性质预测模型是针对特定应用需求筛选设计化学品的有力工具,然而诸多相关建模过程中的测试集划分、交叉验证、算法选择等关键环节普遍存在严谨性不足的问题,模型真实预测性能难以保证。以基团贡献法预测离子液体密度为例,探讨了分子性质预测模型建模过程中数据集划分和交叉验证的重要性,提出了自动基团划分方法并研究了数据集中基团涉及分子个数对预测精度的影响。通过对比五种回归算法（多重线性回归、岭回归、随机森林、支持向量机、神经网络）,基于岭回归的基团贡献模型预测性能最佳,在由1078种离子液体、共计23034个数据点组成的数据集上得到的平均相对误差为1.88%。     

阴离子表面活性剂HLB值与结构关系的拓扑化学研究

用拓扑方法研究阴离子表面活性剂HLB值与分子结构的关系。根据分子结构的特点,用距离矩阵表征分子中原子的链接性。通过回归分析得到一个结构基础明确的定量关系式,其复相关系数为0.999972。用拓扑方法研究阴离子表面活性剂HLB值与分子结构的定量关系是一种新的尝试,补充和完善了QSPR,引入的的拓扑参数Wn、S和Sn与HLB值相关性高,而且计算简便,物理意义明确,提高了预测结果的合理性和准确性,为众多的同名系列化合物HLB值的预测和评价提供了一种新的方式和手段。应用这一定量关系式,不仅能够合理表征阴离子表面活性剂的结构性能关系,而且有助于提示物质结构与性能之间的关系。为阴离子表面活性剂的研究、产品开发、实际生产和综合应用提供重要的参考数据。     

纯物质汽化热的预测

在纯物质常沸点汽化热的剩余函数预测法的基础上,结合并改进W-S方程,推导出预测纯物质在任一温度下的汽化热的新方程。由最基本的物性数据T_b,T_c,P_c,预测245种各类物质7574个温度点的汽化热,与实验值相比较的总绝对平均误差为1.77％,优于原剩余函数法。