Quantitative Structure-Property Relationship Modeling & Computer-Aided Molecular Design: Improvements & Applications

The objective of this work was to develop an integrated capability to design molecules with desired properties. An automated robust genetic algorithm (GA) module has been developed to facilitate the rapid design of new molecules. The generated molecules were scored for the relevant thermophysical properties using non-linear quantitative structure-property relationship (QSPR) models. The descriptor reduction and model development for the QSPR models were implemented using evolutionary algorithms (EA) and artificial neural networks (ANNs). QSPR models for octanol-water partition coefficients (Kow), melting points (MP), normal boiling points (NBP), Gibbs energy of formation, universal quasi-chemical (UNIQUAC) model parameters, and infinite-dilution activity coefficients of cyclohexane and benzene in various organic solvents were developed in this work. To validate the current design methodology, new chemical penetration enhancers (CPEs) for transdermal insulin delivery and new solvents for extractive distillation of the cyclohexane + benzene system were designed. In general, the use of non-linear QSPR models developed in this work provided predictions better than or as good as existing literature models. In particular, the current models for NBP, Gibbs energy of formation, UNIQUAC model parameters, and infinite-dilution activity coefficients have lower errors on external test sets than the literature models. The current models for MP and Kow are comparable with the best models in the literature. The GA-based design framework implemented in this work successfully identified new CPEs for transdermal delivery of insulin, with permeability values comparable to the best CPEs in the literature. Also, new solvents for extractive distillation of cyclohexane/benzene with selectivities two to four times that of the existing solvents were identified. These two case studies validate the ability of the current design framework to identify new molecules with desired target properties.Chemical Engineerin

Aqueous hydrocarbon systems: Experimental measurements and quantitative structure-property relationship modeling

Scope and Method of Study: The experimental objectives of this work were to (a) evaluate existing mutual hydrocarbon-water liquid-liquid equilibrium (LLE) data, and (b) develop an experimental apparatus capable of measuring accurately the hydrocarbon-water (LLE) mutual solubilities. The hydrocarbon-water systems studied included benzene-water, toluene-water, and 3-methylpentane water. The modeling efforts in this study focused on developing quantitative structure-property relationship (QSPR) models for the prediction of infinite-dilution activity coefficient values (gamma infinity i) of hydrocarbon-water systems. Specifically, case studies were constructed to investigate the efficacy of (a) QSPR models using multiple linear regression analyses and non-linear neural networks; and (b) theory-based QSPR model, where the Bader-Gasem activity coefficient model derived from a modified Peng-Robinson equation of state (EOS) is used to model the phase behavior, and QSPR neural networks are used to generalize the EOS binary interaction parameters. The database used in the modeling efforts consisted of 1400 infinite-dilution activity coefficients at temperatures ranging from 283 K to 373 K.Findings and Conclusions: A continuous flow apparatus was utilized to measure the LLE mutual solubilities at temperatures ranging from ambient to 500 K, which is near the three-phase critical end point of the benzene-water and toluene-water systems. The well-documented benzene-water system was used to validate the reliability of the sampling and analytical techniques employed. Generally, adequate agreement was observed for the benzene-water, toluene-water, and 3-methylpentane-water systems with literature data. An error propagation analysis for the three systems indicated maximum expected uncertainties of 4% and 8% in the water phase and organic phase solubility measurements, respectively. In general, the use of non-linear QSPR models developed in this work were satisfactory and compared favorably to the majority of predictive models found in literature; however, these model did not account for temperature dependence. The Bader-Gasem activity coefficient model fitted with QSPR generalized binary interactions was capable of providing accurate predictions for the infinite-dilution activity coefficients of hydrocarbons in water. Careful validation of the model predictions over the full temperature range of the data considered yielded absolute average deviations of 3.4% in ln gamma infinity i and 15% in gamma infinity i, which is about twice the estimated experimental uncertainty. This study provides valuable LLE mutual solubility data and further demonstrates the effectiveness of theory-framed QSPR modeling of thermophysical properties

Quantitative structure-property relationship generalized activity coefficient models

Phase behavior properties of chemical species and their mixtures are essential to design chemical processes involving multiple phases. Thermodynamic models are used in phase equilibria calculations to determine properties, such as phase compositions and partition coefficients at specific temperatures and pressures. In the absence of experimental data, generalized models are employed to predict phase equilibria properties.The two main objectives of this study are to (1) develop improved generalized models for vapor-liquid equilibria (VLE) and liquid-liquid equilibria (LLE) property predictions using a theory-framed quantitative structure-property relationship (QSPR) modeling approach and (2) implement a new modification to the widely used nonrandom two-liquid (NRTL) activity coefficient model to reduce parameters correlation, which is a limitation of the original model.In this work, we assembled two databases consisting of 916 binary VLE and 342 binary low-temperature LLE data. Data regression analyses were performed to determine the interaction parameters of various activity coefficient models. Structural descriptors of the molecules were generated and used in developing QSPR models to estimate the regressed interaction parameters. The developed QSPR models for VLE systems provided phase equilibria property predictions within twice the errors obtained through the data regression analyses for VLE systems. For LLE systems, the QSPR models resulted in approximately three to four times the errors found from the regression analyses. Further, our methodology provides a priori and easily implementable QSPR models with a wider applicability range than that of the group-contribution model, UNIFAC.The newly modified model proposed in this work reduced the NRTL model to a one-parameter model and eliminated the parameter correlation. The original and modified NRTL models yield comparable accuracies in representing experimental equilibrium properties. The benefits of our modification include easy generalizability of the parameters, ability to classify VLE behaviors based on a single model parameter and fewer convergence problems in parameter regressions

On the prediction of partition coefficients using the statistical associating fluid theory underpinned by quantum mechanical calculations

The thermodynamic modelling of phase equilibrium is of central importance in chemical engineering applications. The design, operation and develop- ment of new chemical processes is based to a large extent on the knowledge of the equilibrium that occurs between co-existing fluid phases. Where re- liable experimental data at required process conditions is unavailable, an understanding of the molecular description of condensed phase matter is key to predicting the thermodynamic properties of these fluid systems. To this end, numerous models and theories have been developed that seek to link microscopic intermolecular interactions with bulk macroscopic thermo- dynamic properties. In this thesis, two such constructs for the prediction of phase equilibrium are considered. The empirical linear solvation energy relationship (LSER) that relates specific/unspecific intermolecular interac- tions to infinite dilution solute properties, and equations of state (EoS) for the prediciton of vapour-liquid and liquid-liquid equilibrium. The LSER model utilises hydrogen bond acceptor/donor parameters (A and B) alongside polarisability (S), volume (V) and molar refraction (E) param- eters to describe various solute properties. In this study, the prediciton of solute infinite dilution partititon coefficient is of particular interest. While the V and E parameters can be obtained from molecular structure calcula- tions that account for the number of atoms and bonds in a molecule, the re- maining LSER parameters are usually derived from chromatographic experiments. However, successive studies have successfully correlated and pre- dicted the hydrogen bonding parameters from quantum mechanical (QM) calculated molecular properties, enabling the rapid calculation of infinite dilution solute properties in the so-called QM/LSER approach. In this the- sis, two independent linear regression relationships that relate theoretically calculated hydrogen bond stabilisation energies at a donor and/or acceptor site(s) to experimental hydrogen bonding ability of a solute molecule have been determined. Once obtained, the solute hydrogen bonding parameters are used in conjunction with dispersion and volume parameters in the LSER to obtain solute partition coefficients. Using this approach ,the octanol/wa- ter partition coefficients of various molecules have been estimated, of this, the absolute average error of a sub-set of straight chained, mono-functional solute molecules has been determined to be 23.04% when compared to ex- perimental data. The second approach to modeling condensed phased matter is based on the statistical associating fluid theory (SAFT), a molecular-based equation of state with a foundation in statistical mechanics. Here, a recently devel- oped group-contribution version i.e., SAFT-1 is considered. The SAFT-1 EoS has been successfully applied in the prediction of the octanol/water patition coefficients of a range of solute molecules that include n-alkane, n-alkene, 2- ketone and n-amine molecules. Where the average absolute error of SAFT- 1 predicitons when compared to experimental data is found to be 13.20%. However, as with other EoS, SAFT-1 is dependent on experimental data re- quired to parameterise the various groups that make up the fluid/fluid mix- ture under investigation. The aim of this work is to increase the predictive ability of SAFT-1 by reducing dependence on experimental data, whereby in- stead of equilibrium data, solute partition coefficients estimated using the QM/LSER method are used to parameterise the relevant molecular groups. In the final part of the thesis, the proposed hypothesis of combining the QM/LSER and SAFT-1 methods is tested with the aim of predicting the phase behaviour of binary mixtures. The method relies on the calculation of partition coefficients using QM and LSER, the calculated partition coef- ficients are then used to parameterise the unlike group-group interactions required for the prediction of binary mixture behaviour in SAFT-1. This methodology has been validated using the n-aldehyde and 2-ketone chemi- cal families, where using QM/LSER to parameterise SAFT-1 has been found to achieve results that are comparative to the classical empirical approach of parameterising the SAFT-1 EoS when predicting binary phase behaviour. The unlike group interaction parameters for the SAFT-1 EoS have been suc- cessfully parameterised using partition coefficient data estimated from the- oretically calculated quantum mechanical molecular properties. However, the solutes considered in this study are limited to linear mono-functional molecules. The reason for this limitation is two fold. Firstly, predicting hydrogen bond parameters of multi-functional molecules is unreliable mainly as a consequence of polarisation of H-bond sites due to the proximity of functional groups. Therefore a better understanding of how polarisation affects hydrogen bonding is required. Secondly, within SAFT-1 the major- ity of available groups are for modeling linear mono-functional molecules. However there is continuing work to model both branched and multifunc- tional molecules. Once both of these concerns are effectively dealt with, the proposed methodology can be used to characterize a wider range of SAFT- 1 groups and predict thermodynamic behaviour of molecules based on QM molecular calculations.Open Acces

Representation/prediction of physico-chemical properties of ionic liquids through different computational methods.

Ph. D. University of KwaZulu-Natal, Durban 2014.The “green” industrial chemical processes are of great interest to scientists and engineers due to elimination of environmental pollution, especially air pollution. One of the most important air pollutants is class of materials called volatile organic compounds (VOCs) which are widely used in different industrial chemical processes. The recent research has revealed that ionic liquids (ILs) are generally the best possible alternative to the conventional solvents; because in general, the ILs have interesting properties such as very low vapor pressure, nonflammability, and high physical and chemical stability. Ionic liquids are constituted of ions, typically a cation and an anion, and their thermophysical properties are strongly dependent on the type and chemical structure of the cation and anion. As a result, in theory, they can be designed for specific applications with certain properties by choosing the appropriate combination of anion/cation pair. For this purpose, a predictive model is required to estimate the target property based on the chemical structure of ions. At the initial step of this study, the NIST Standard Reference Database #103b as well as the published papers in the literature was chosen as the source of experimental data of ionic liquids. As a result, a large database was collected covering several thermophysical properties of ILs. Thereafter, the collected data were examined carefully and the duplicated and erroneous data were screened. Speed of sound, heat capacity, refractive index, viscosity, infinite dilution activity coefficient () , and critical temperature of various ionic liquids were modeled by means of two well-known property estimation methods, Group Contribution (GC) and Quantitative Structure-Property Relationship (QSPR) methods. These methods were combined with different computational and regression techniques such as genetic function approximation (GFA) and least square support vector machine (LS-SVM). The combined routines then were applied to select reasonable number of parameters from thousands of variables and to develop the predictive models for representation/prediction of chosen temperature-dependent thermophysical properties of ionic liquids. Speed of sound in ionic liquids was modeled successfully and two models were developed, one GC and one QSPR model. These models were the first GC and QSPR models developed for this property in the literature. Both models had better accuracy in terms of average absolute relative deviation (the AARD% of 0.36 for the GC and 0.92% for the QSPR models over 41 ILs) and covered a wider range of ionic liquids compared with the previous models published (AARD% of 1.96% over 14 ILs) and consequently, they were more applicable. Liquid heat capacity of ionic liquids was studied and one GC and one QSPR model were developed. Both models covered 82 ILs which was a larger number of ionic liquids compared with the best available model in the literature (32 ILs with an AARD% of 0.34%) and had relatively low AARD%. The AARD% of the models was 1.68% and 1.70% for the GC and QSPR models, respectively. In addition, the QSPR model was the first model developed for this property through the QSPR approach. For the refractive index of ionic liquids, little attention had been given to modeling and consequently, one new GC (AARD% = 0.34%) and the first QSPR (AARD% = 0.51%) models were developed to predict this property using the experimental data for 97 ionic liquids. Both models covered a wider range of ionic liquids and showed very good prediction ability compared with the best available model (an AARD% of 0.18% for 24 ILs). Viscosity of Fluorine-containing ionic liquids was studied because the insertion of fluorinated moieties in the molecular structure of ionic liquids could result in reduction of viscosity. As a result, one QSPR (AARD% = 2.91%) and two GC models were developed using two different databases, one with fewer number of ionic liquids but with more reliable data (AARD% = 3.23%), the one with larger number of ionic liquids but with lower reliability (AARD% = 4.85%). All of the models developed had better prediction ability compared with the previous models and covered a wider range of fluorinated ionic liquids. Infinite dilution activity coefficient (γ∞) of organic solutes was modeled by developing six different models for different types of solutes (alkane, alkene, aromatic, etc.). The model developed were the first GC models for the prediction of γ∞ of solutes in ionic liquids. They were much easier to use, more comprehensive, and much more accurate compared with the UNIFAC model. Ultimately, the theoretical critical temperature (Tc) of ionic liquids was tried to model using the GC and QSPR approaches. The experimental data of surface tension of 106 ionic liquids were used to calculate the critical temperature and then, these values were used to develop the models. It was found that the only available model in the literature was not accurate and predictive enough when its output was compared with the abovementioned Tc values. In addition, it was found that both of the models developed were not predictive enough to calculate the Tc of various types of ionic liquids as the models were developed using a few number of ionic liquids; however both models were accurate enough to fit the used values of Tc. The GC model has an AARD% of 5.17% and the QSPR model showed the AARD% of 4.69%. It this thesis, much larger databases were used to develop the models compared with the models published previously in the literature. It was found that thermophysical properties of ionic liquids can be modeled fairly well by combination of the GC or QSPR methods with an appropriate regression technique. In addition, the developed models improved significantly the quality of fit and predictions for a wider range of ionic liquids compared with the previous models. Consequently, the models proposed are more predictive and can be used to design the ionic liquids with desired property for specific applications.Please note that the symbol that appears after "coefficient " that appears in brackets "( )" could not be copied. Please refer to page i of the thesis abstract to look at the symbol