QSPR Approach Research Articles

Prediction of Ignition Delay Times for Amine-based Liquid Propellants through a QSPR Approach

Prediction of Ignition Delay Times for Amine-based Liquid Propellants through a QSPR Approach

Reliable Prediction of Caco-2 Permeability by Supervised Recursive Machine Learning Approaches.

The heterogeneity of the Caco-2 cell line and differences in experimental protocols for permeability assessment using this cell-based method have resulted in the high variability of Caco-2 permeability measurements. These problems have limited the generation of large datasets to develop accurate and applicable regression models. This study presents a QSPR approach developed on the KNIME analytical platform and based on a structurally diverse dataset of over 4900 molecules. Interpretable models were obtained using random forest supervised recursive algorithms for data cleaning and feature selection. The development of a conditional consensus model based on regional and global regression random forest produced models with RMSE values between 0.43–0.51 for all validation sets. The potential applicability of the model as a surrogate for the in vitro Caco-2 assay was demonstrated through blind prediction of 32 drugs recommended by the International Council for the Harmonization of Technical Requirements for Pharmaceuticals (ICH) for validation of in vitro permeability methods. The model was validated for the preliminary estimation of the BCS/BDDCS class. The KNIME workflow developed to automate new drug prediction is freely available. The results suggest that this automated prediction platform is a reliable tool for identifying the most promising compounds with high intestinal permeability during the early stages of drug discovery.

Prediction of Optimal Conditions of Hydrogenation Reaction Using the Likelihood Ranking Approach.

The selection of experimental conditions leading to a reasonable yield is an important and essential element for the automated development of a synthesis plan and the subsequent synthesis of the target compound. The classical QSPR approach, requiring one-to-one correspondence between chemical structure and a target property, can be used for optimal reaction conditions prediction only on a limited scale when only one condition component (e.g., catalyst or solvent) is considered. However, a particular reaction can proceed under several different conditions. In this paper, we describe the Likelihood Ranking Model representing an artificial neural network that outputs a list of different conditions ranked according to their suitability to a given chemical transformation. Benchmarking calculations demonstrated that our model outperformed some popular approaches to the theoretical assessment of reaction conditions, such as k Nearest Neighbors, and a recurrent artificial neural network performance prediction of condition components (reagents, solvents, catalysts, and temperature). The ability of the Likelihood Ranking model trained on a hydrogenation reactions dataset, (~42,000 reactions) from Reaxys® database, to propose conditions that led to the desired product was validated experimentally on a set of three reactions with rich selectivity issues.

Inhibitory Effect of Some Methylxanthines on Copper Corrosion in 1M HNO3: Experimental, DFT and QSPR Studies

Inhibition corrosion of metals by using organic compounds has become an unavoidable means. So, in this work, the effect of methylxanthines on copper corrosion inhibition in 1M HNO3 was investigated by mass loss measurements and by two theoretical approaches: Density Functional Theory (DFT) and Quantitative Structure Property Relationship (QSPR.) Quantum chemical calculations based on DFT at the B3LYP/6-31G(d) level permit to establish a correlation between the quantum chemical parameters and the experimental inhibition efficiency (IE %). It was found that inhibition efficiencies increase with increasing temperature and immersion time. In addition, the QSPR approach was used to find the best set of parameters for each molecule. This set of parameters make it possible to characterize the inhibition performance of the tested molecules solution significantly. The theoretical calculations are consistent with the experimental results.

Prediction of ionic conductivity of imidazolium-based ionic liquids at different temperatures using multiple linear regression and support vector machine algorithms

The conductivity of various imidazolium-based ILs has been predictedviaQSPR approach using MLR and SVM regression coupled with stepwise model-building. This will aid the screening of suitable ILs with desired conductivity for specific applications.

Design and prediction of high potentansa-zirconocene catalyst for olefin polymerizations: combined DFT calculations and QSPR approach

Density functional calculations were carried out to predict activities, regio- and stereoselectivity, and to design newansa-zirconocene catalysts for olefin polymerizations.

Synthesis of covalent organic frameworks using sustainable solvents and machine learning

Covalent organic frameworks have been prepared in sustainable solvents by a solvothermal method, and their porosity and crystallinity were predicted using QSPR and machine learning approaches.

Heat of Formation Assessment of Organic Azido Compounds Used as Green Energetic Plasticizers by QSPR Approaches

AbstractQuantitative structure‐property relationship (QSPR) approaches were applied as a tool for assessment of the standard enthalpy of formation of 40 organic azido compounds with different molecular structure which are used as energetic plasticizers in energetic material formulations. Chemical structures of these compounds were encoded into several sets of molecular descriptors, which represent various physical and chemical properties of these molecules. A total number of 1475 molecular descriptors of 22 different types were selected and calculated. Then a stepwise regression method was applied to determine the most relevant molecular descriptors which have significant effects on the ΔH°f of azido compounds. Finally, the best subsets regression method and also the response plot of PLS regression is used to evaluate the predictive power of the selected molecular descriptors and find out the possibility to reduce the dimension of the initially developed model. The finally derived models displayed a good predictive ability arising from the determination coefficient corresponding to the leave one out (LOO) cross validation technique (R2=0.9821, 0.9962 and =0.9907, 0.9851 respectively). Moreover, the obtained results of this investigation are compared with results which have been calculated using “Energetic Materials Designing Bench (EMDB), Version 1.0” software package.

Quantitative structure–property relationship modeling for the prediction of hydrophilic drug entrapment in liposomes for lung targeted delivery

A QSPR approach to the development of targeted formulation could save time and cost by early prediction of the most influential ingredient properties.

Investigation of the most effective molecular descriptors on the thermal behaviour of energetic azido-ester plasticizers through QSPR approach

In this work, the most effective molecular descriptors on the thermal behaviour of energetic azido-ester plasticizers are investigated through quantitative structure–property relationship approach. At first, a new simple and reliable correlation is developed for predicting thermal decomposition temperature (T d) of energetic azido-ester plasticizers through multiple linear regression method. The determination coefficient of the derived correlation is 0.950, and it has root mean square deviation (RMSD) and average absolute deviation (AAD) of 2.74 and 2.09 °C, respectively. The internal and external validation method is shown that the developed correlation has good predictive ability. Then, the relationship between glass transition temperature (T G) of energetic azido-ester plasticizers and their T d is studied. This study shows that the optimum elemental composition, number of ester groups, number of azido groups and the presence of aromatic fragments are the important molecular descriptors as well as several non-additive structural parameters which have significant effect on the thermal behaviour of energetic azido-ester plasticizers. The determination coefficient of this correlation is 0.971, and it has RMSD and AAD of 3.70 and 2.67 °C, respectively. These results could be used to design ideal energetic azido-ester plasticizers.

Determination of pKa and the corresponding structures of quinclorac using combined experimental and theoretical approaches

As an emerging environmental contaminant, the herbicide quinclorac has attracted much attention in recent years. However, a very fundamental issue, the acid dissociation of quinclorac has not yet to be studied in detail. Herein, the pKa value and the corresponding structures of quinclorac were systematically investigated using combined experimental and theoretical approaches. The experimental pKa of quinclorac was determined by the spectrophotometric method to be 2.65 at 25 °C with ionic strength of 0.05 M, and was corrected to be 2.56 at ionic strength of zero. The molecular structures of quinclorac were then located by employing the DFT calculation. The anionic quinclorac was directly located with the carboxylic group perpendicular to the aromatic ring, while neutral quinclorac was found to be the equivalent twin structures. The result was further confirmed by analyzing the UV/Vis and MS-MS2 spectra from both experimental and theoretical viewpoints. By employing the QSPR approach, the theoretical pKa of QCR was determined to be 2.50, which is excellent agreement with the experimental result obtained herein. The protonation of QCR at the carboxylic group instead of the quinoline structure was attributed to the weak electronegative property of nitrogen atom induced by the electron-withdrawing groups. It is anticipated that this work could not only help in gaining a deep insight into the acid dissociation of quinclorac but also offering the key information on its reaction and interaction with others.

Anticorrosion Properties of 8-Oxyquinoline Derivatives

For the development of new complex inhibitors of acid corrosion in steels within the framework of the QSPR approach, we study the anticorrosion properties of the samples of arylsulfonyl esters of 8-oxyquinoline in sulfuric acid and compare their electronic characteristics with the data of quantumchemical analysis in the РМ7 semiempirical approximation. The following series of esters were investigated: naphthalene-1-yl-benzosulfonate (1), 4-methylbenzosulfonate (2), 4-nitrobenzosulfonate (3), 2-methylbenzosulfonate (4), naphthalene-1-sulfonate (5) and, for the sake of comparison, the original compound, i.e., 8-oxyquinoline (0). Samples of 20 steel were tested in a model corrosive medium by the gravimetric method under the following conditions: 0.1 М solution of sulfuric acid, a temperature of the medium equal to 55°С, the duration of experiment equal to 2.5 h (in the vibration mode), and a concentration of sulfonyl esters of 0.01 mole/liter. We obtain the following sequence of increase in the anticorrosion activity of these compounds: (0) << (1) < (4) < (2) < (3) < (5), which completely agrees with the changes in the molecular volume and the Van-der-Waals surface area of the molecules computed in the РМ7 approximation. This effect demonstrates the predominance of these descriptors in analyzing the influence of the structure of esters on their anticorrosion properties. It is also established that all these compounds are potential fungicides and, hence, can be used in complex inhibiting compositions as admixtures to protective lacquer anticorrosion coatings.

Prediction of Blood-to-Brain Barrier Partitioning of Drugs and Organic Compounds Using a QSPR Approach

Prediction of Blood-to-Brain Barrier Partitioning of Drugs and Organic Compounds Using a QSPR Approach

A quasi-SMILES based QSPR Approach towards the prediction of adsorption energy of Ziegler − Natta catalysts for propylene polymerization

QSPR models are well known for reliable prediction of physicochemical properties. The quasi-SMILES code of the internal donor molecules are derived from the different molecular fragments of phthalates, 1,3-diethers and malonates. The adsorption energy has been modeled with the optimal descriptor derived from such quasi-SMILES codes. QSPR model was successfully applied for designing 24 new internal donors for the ZN catalyzed propylene polymerization with better adsorption energies. The science of quasi-QSPR model is not sufficient for an abstract, therefore described in detail in the paper so that one can learn how to develop the quasi-SMILES based models.

Sensitivity and Performance of Energetic Materials

AbstractThis paper provides an overview of the main developments over the past nine years in the study of the sensitivity of energetic materials (EM) to impact, shock, friction, electric spark, laser beams, and heat. Attention is also paid to performance and to its calculation methods. Summaries are provided of the relationships between sensitivity and performance, the best representations for the calculation methods of performance being the volume heat of explosion or the product of crystal density and the square of detonation velocity. On the basis of current knowledge, it is possible to state that a single universal relationship between molecular structure and initiation reactivity does not yet exist. It is confirmed that increasing the explosive strength is usually accompanied by an increase in the sensitivity. In the case of nitramines this rule is totally valid for friction sensitivity, but for impact sensitivity there are exceptions to the rule, and with 1,3,5‐trinitro‐1,3,5‐triazepane, 1,3,5‐trinitro‐1,3,5‐triazinane, β‐1,3,5,7‐tetranitro‐1,3,5,7‐tetrazocane, and the α‐, β‐ and ε‐polymorphs of 2,4,6,8,10,12‐hexanitro‐2,4,6,8,10,12‐hexaazaisowurtzitane the relationship works in the opposite direction. With respect to the QSPR approach there might be reasonably good predictions but it provides little insight into the physics and chemistry involved in the process of initiation.

A neural network for predicting normal boiling point of pure refrigerants using molecular groups and a topological index

An artificial neuron network based on genetic algorithm is presented to predict the normal boiling point (Tb) of refrigerants from 16 molecular groups and a topological index. The 16 molecular groups used in this paper can cover most refrigerants or working fluids in refrigeration, heat pump and organic Rankine cycle; the chosen topological index is able to distinguish all the refrigerant isomers. A total of 334 data points from previous experiments are used to create this network. The calculated results, which are based on a developed numerical method, show a good agreement with experimental data; the average absolute deviations for training, validation and test sets are 1.83%, 1.77%, 2.13%, respectively. A performance comparison between the developed numerical model and the other two existing models, namely QSPR approach and UNIFAC group contribution method, shows that the proposed model can predict Tb of refrigerants in a better accord with experimental data.

Correlations of limiting oxygen index with structural polyphosphoester features by QSPR approaches

Fire retardant materials diminish the hazard to life from fire. Polyphosphonates and polyphosphates display good flame retardancy and attractive plasticizing properties, being an important class of organophosphorus based polymer additives. Properties of previously synthesized polyphosphoesters are presented here, being simulated as monomers. In this quantitative structure–property relationship work, the flammability was expressed by limiting oxygen index (LOI) values, which were determined experimentally. Two types of chiral structures were found by molecular mechanics calculations using the MMFF94s force field for half of the monomers, consequently, two datasets were built. Structural parameters were calculated for the minimum energy structures and were related to the LOI values by multiple linear regression (MLR), artificial neural networks (ANNs), and support vector machines (SVMs). MLR calculations were combined with a genetic algorithm for variable selection. Stable and predictive MLR models in terms of 2D autocorrelation parameters weighed by atomic polarizabilities and of 3D-Morse descriptors were obtained. Somewhat inferior fits were found in the nonlinear modeling by ANNs and SVMs with the same set of descriptors. Monomers including R chiral structures gave more stable and predictive models compared to the S isomers in all approaches. Monomer geometry influences the flame retardancy, being favorable for R isomers.

A study of the relationship between the electronic structure and the thermodynamic properties of individual compounds

464 The study of reaction mechanisms, development of chemical technology, search for biologically active substances, and other topical physicochemical investi� gations require knowledge of the quantitative charac� teristics of compounds. These include key thermody� namic properties, such as the entropy, enthalpy, free energy, and heat capacity. Calculation of the thermo� dynamic properties of substances is associated with certain theoretical difficulties, is extremely resource� intensive, and usually does not provide the desired accuracy for the required quantity. This is especially the case for intermediates. Therefore, this field is dominated by phenomenological models, the most popular of which are additive models (additivegroup approach and macroincrementation). These models require parameterization and are not free from sys� tematic errors, a feature that requires the introduction of corrective procedures, for example, corrections for nonvalence interactions. Construction of models for calculating the thermo� dynamic properties with acceptable error for sub� stances consisting of large polyatomic molecules is a challenging task. Most of them are based on the assumption that the structure of a fragment is related to the contribution from this fragment to the given property of the molecule (quantitative structure- property relationship, QSPR). This assumption is adopted as a postulate, since it is impossible to theo� retically predict the contribution from a fragment based on its type or the relative arrangement of its atoms ("structure" descriptor). In such models, the introduction of descriptors involves certain arbitrari� ness, a circumstance that prevents ensuring the stated accuracy in predicting the properties. Development of a phenomenological model (1, 2) begins with the selection of the experimentally mea� sured properties of reference substances, followed by the partition of the samples into two sets, training and testing. The model is parameterized using the training set, whereas the testing set is employed to estimate the errors of the respective quantitative correlations, and, if required, corrective procedures are introduced into the model. The emergence of significant errors and failures outside the training set requires a changeover from searching for possible empirical relationships in the QSPR approach to constructing phenomenologi� cal models based on rigorous theorems and postulates of quantum mechanics. The use of such methods enables, on the one hand, to specify the confidence interval for the values obtained (based on the approxi� mations made in the derivation of the relationships) and, on the other, to determine the limits of applica� bility of the model.

A QSPR approach for the fast estimation of DFT/NBO partial atomic charges

The prediction of DFT Natural Bond Orbital (NBO) atomic charges was investigated with machine learning techniques and 2D atomic descriptors. Atomic descriptors were based on atom types (defined by the element and number of neighbour atoms) and topological interatomic distances (number of bonds), so that predictions do not require 3D structures and can be calculated very rapidly. Separate models were built for hydrogen atoms (12,541 atoms in the training set) and non-hydrogen atoms (22,764 atoms in the training set). The best results were achieved with feed-forward neural networks comprising 136 or 155 input neurons (for H atoms and non-H atoms, respectively) and 6 hidden neurons. Predictions for 4178 H atoms and 7587 non-H atoms in independent test sets were obtained with Q2=0.987/RMSE=0.0080/MAE=0.0054 and Q2=0.996/RMSE=0.0273/MAE=0.0182, respectively. The results show how QSPR approaches can provide fast access to accurate estimations of DFT–NBO charges. Such high-level theoretical quantum calculations can thus be used in large-scale applications that otherwise would not afford the intrinsic computational cost.

An improved QSPR method based on support vector machine applying rational sample data selection and genetic algorithm-controlled training parameters optimization

An improved QSPR method based on support vector machine (SVM) applying rational sample data selection and genetic algorithm (GA)-controlled training parameters optimization was developed to study the standard formation Gibbs free energy of 78 kinds of acyclic alkanes. The SVMs were trained applying the standard regression algorithm based on quadratic programming theory, and the Gaussian radial basis kernel function (RBKF) was employed in the training process. Meanwhile, eight well-known topological indices were used as structural descriptors for each alkane molecule, and they were also considered to be the potential input variables for the proposed QSPR models. Subsequently, by optimizing the ε parameter in insensitive loss function, the penal factor C, the σ parameter in RBKF and the input variable representations simultaneously via GA, a novel QSPR approach based on the combination of GA and SVM was proposed to improve the prediction results of the independent external test samples. For independent external test samples selected randomly prior to QSPR model development, an improved predictive modeling method based on SVM was achieved by rationally selecting the training and the internal test data set with sphere exclusion algorithm and optimizing the SVM training parameters by the proposed GA method.For comparing purpose, partial least square (PLS) regression method was also used as another QSPR modeling tool for the experimental data set. Moreover, to verify the improved modeling method in a more general way, two mathematically simulated QSPR data sets were built to confirm its validity.