Study of Acid Suppressed Thickener Technology Using Density Functional Theory and Machine Learning Techniques

Hydrophobically modified ethylene oxide urethane (HEUR) rheology modifiers, which are water-based polyurethane formulations manufactured by Dow Coating Materials, a division of the Dow Chemical Company, are often added to interior and exterior water-based Latex paint formulations to control their viscosity. The thickening efficiency of the HEUR rheol-ogy modifier is controlled by the pH of the solvent, as this affects the protonation-deprotonation equilibrium of the amine hydrophobe group at the end of the rheology modi-fier polymer chain. The principal quantity characterizing this equilibrium is the acid disso-ciation constant (pKa) of the hydrophobe group, which identifies the transition between high and low viscosity of the suspension. To gain a better understanding of the functioning of the hydrophobe molecular groups, and to develop novel hydrophobes that meet specific per-formance characteristics, it is important to accurately predict the pKa based on first princi-ples calculations, and use it as a first evaluation criterion for a rapid screening of candidate hydrophobe molecules. A main source of error in the pKa calculation is the value of solvation free energy of the molecule in its charged state. We therefore develop new methods to increase the accuracy of the solvation free energy calculation for charged species without excessive increase the computational expense. This includes a hybrid cluster-continuum model approach, where explicit solvent molecules are added to the traditionally employed continuum solvation model, and a molecular dynamics (MD sampling procedure that eliminates the costly ener-gy minimization step. Using test molecules for pKa calculations, we systematically exam-ine the convergence behavior in terms of number of explicit water molecules that need to be included in the cluster-continuum model, the influence of the dielectric constant attributed to the continuum, and the placement of a counter ion for charge neutrality for the accurate calculation of the solvation free energy. We establish that the MD sampling method yields results comparable the energy minimization procedure during density functional theory (DFT) calculations, but at 100 times the speed. When calculating the solvation free energy and the pKa calculation of a known hydrophobe, ethoxylated bis(2-ethylhexy)amine, we find that including explicit water molecules and a fragment of the latex polymer in its local en-vironment both significantly improve the results. Finally we develop an informatics-based approach that employs a transferable machine learning (ML) model, trained and validated on a limited amount of experimental data, to predict the solvation free energies of new ionic species at a reasonable computational cost. We compare three different ML methods – linear ridge regression, support vector regression and random forest regression, and find that the model trained by the random forest regres-sion method yields the predictions with the lowest mean absolute error. A feature selection analysis shows that the atomic fraction feature, which reflects the chemical constitution of the hydrophobe, plays the most important role in the solvation free energy prediction. Add-ing the Wiener index, a measure of the molecular topology, and the solvent accessible sur-face area of the molecules further improve the performance of the model. Accordingly, our ML model predicts the solvation energies of ionic species, including our test hydrophobe molecule, with similar accuracy as atomistic modeling using first-principles calculations.PHDMaterials Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145967/1/wuwenkun_1.pd

Solvation thermodynamics of organic molecules by the molecular integral equation theory : approaching chemical accuracy

The integral equation theory (IET) of molecular liquids has been an active area of academic research in theoretical and computational physical chemistry for over 40 years because it provides a consistent theoretical framework to describe the structural and thermodynamic properties of liquid-phase solutions. The theory can describe pure and mixed solvent systems (including anisotropic and nonequilibrium systems) and has already been used for theoretical studies of a vast range of problems in chemical physics / physical chemistry, molecular biology, colloids, soft matter, and electrochemistry. A consider- able advantage of IET is that it can be used to study speci fi c solute − solvent interactions, unlike continuum solvent models, but yet it requires considerably less computational expense than explicit solvent simulations

Computational studies of phosphate clusters and bioglasses

The aim of this PhD research project is to investigate the aqueous behaviours of phosphate species and the structure of bioactive phosphate glasses via computational modelling. Density functional theory has been employed as the core methodology throughout the project. The molecular structures of bioactive ternary phosphate-based glasses with compositions (P2O5)0.45(CaO)x(Na2O)0.55-x, where x = 0.30, 0.35 and 0.45, have been explored by a range of Car-Parrinello molecular simulations. Careful structure analysis has been carried out in order to provide an accurate description of the local structure and properties of these important materials for biomedical applications. This is followed by the Car–Parrinello molecular dynamics simulations of the first hydration shell structures. Extensive simulations provide new insights into hydrogen transfer and intermolecular and hydration properties of these important aqueous species. Apart from ab-initio molecular dynamics calculations, first principles density functional theory calculations with a cluster-continuum solvation model have been used to evaluate the relative energetic stabilities of various phosphate oligomers in an aqueous environment. As a result, an illustrative picture of different aspects of phosphate species related to the dissolution behaviour of bioactive phosphate glasses was built up from various angles, which will form a solid foundation for further computational studies of bioactive phosphate glasses in the future

Computergestützte Vorhersage von thermodynamischen Eigenschaften organischer Moleküle in wässrigen Lösungen

We showed that the poor accuracy of hydration thermodynamics calculations with a molecular integral equation theory, Reference Interaction Site Model (RISM), can be considerably improved with a set of molecular structural corrections. In this thesis we developed a novel hybrid RISM-based method for calculation of hydration thermodynamics, the Structural Descriptors Correction (SDC) model (RISM-SDC). The method uses a thermodynamic quantity obtained by RISM as an initial approximation and a set of corrections to decrease the error of the calculated parameter. Each correction in the RISM-SDC model can be represented as a structural descriptor (Di) multiplied by the corresponding correction coefficient (ai). One important descriptor (D1) is the dimensionless partial molar volume calculated by RISM. The rest of the structural descriptors correspond to the number of specific molecular fragments (double bonds, aromatic rings, electron-donating/withdrawing substituents, etc.). The correction coefficients {ai} are found by training the model on a set of monofunctional compounds. For the first time, we showed that the RISM-SDC model allows to achieve the chemical accuracy of solvation thermodynamics predictions within the RISM approach, that has been a challenge for over 40 years. In this thesis we demonstrated the high efficiency of the RISM-SDC model for predicting important hydration thermodynamic quantities, hydration free energy (HFE) and partial molar volume (PMV).Wir haben gezeigt, dass die geringe Genauigkeit der Berechungen zur Hydrationsthermodynamik mit der molekularen Integralgleichungstheory, Reference Interaction Site Modell (RISM), in hohem Maße verbessert werden kann durch Einführung eines Satzes molekularer struktureller Korrekturen. In dieser Arbeit entwickelten wir eine neue RISM-basierte Hybridmethode für die Berechnung von Hydrationsthermodynamik, genannt Structural Descriptors Correction (SDC) Modell (RISM-SDC). Die Methode nutzt eine thermodynamische Größe, die durch RISM erhalten wird, als initiale Näherung und einen Satz von Korrekturen um den Fehler des berechneten Parameters zu verringern. Jede Korrektur im RISM-SDC Modell kann als struktureller Deskriptor (Di) mutlipliziert mit dem zugehörigen Korrekturkoeffizenten (ai) dargestellt werden. Ein wichtiger Deskriptor (D1) ist das dimensionslose partielle molare Volumen berechnet durch RISM. Die anderen strukturellen Deskriptoren entsprechen der Anzahl der spezifischen molekularen Fragmente (Doppelbindungen, aromatische Ringe, Elektron-spendende/entziehende Substituenten, etc.). Die Korrekturkoeffizienten {ai} wurden durch Anwendung des Modells auf einen Satz monofunktionaler Verbindungen ermittelt. Erstmals konnten wir zeigen, dass das RISM-SDC Modell die chemische Genauigkeit von Lösungsthermodynamik Vorhersagen mit der RISM Methode erlaubt; dies war eine Herausforderung für über 40 Jahre. In dieser Arbeit haben wir die hohe Effizienz des RISM-SDC Modells demonstriert für die Vorhersage wichtiger thermodynamischer Größen der Hydration wie der Freien Energie der Hydration (hydration free energy, HFE) und des partiellen molaren Volumens (partial molar volume, PMV)

Investigation of catalysis by bacterial RNase P via LNA and other modifications at the scissile phosphodiester

We analyzed cleavage of precursor tRNAs with an LNA, 2′-OCH3, 2′-H or 2′-F modification at the canonical (c0) site by bacterial RNase P. We infer that the major function of the 2′-substituent at nt −1 during substrate ground state binding is to accept an H-bond. Cleavage of the LNA substrate at the c0 site by Escherichia coli RNase P RNA demonstrated that the transition state for cleavage can in principle be achieved with a locked C3′ -endo ribose and without the H-bond donor function of the 2′-substituent. LNA and 2′-OCH3 suppressed processing at the major aberrant m−1 site; instead, the m+1 (nt +1/+2) site was utilized. For the LNA variant, parallel pathways leading to cleavage at the c0 and m+1 sites had different pH profiles, with a higher Mg2+ requirement for c0 versus m+1 cleavage. The strong catalytic defect for LNA and 2′-OCH3 supports a model where the extra methylene (LNA) or methyl group (2′-OCH3) causes a steric interference with a nearby bound catalytic Mg2+ during its recoordination on the way to the transition state for cleavage. The presence of the protein cofactor suppressed the ground state binding defects, but not the catalytic defects

Large and multi scale mechanistic modeling of Diels-Alder reactions

The [4+2] cycloaddition reaction between conjugated dienes and substituted alkenes is known as the Diels-Alder (DA) reaction, in honor of two German chemists, Otto Diels and Kurt Alder, who first reported this marvelous chemical transformation. The DA reaction is one of the most popular reactions in organic chemistry, allowing for the regio- and stereospecific establishment of six-membered rings with up to four stereogenic centers. This pericyclic reaction has found many applications in areas as diverse as natural products chemistry, polymer chemistry, and agrochemistry. Over the past decades, the mechanism of the Diels-Alder (DA) reaction has been the subject of numerous studies, dealing with questions as diverse as the mechanistic pathway, the synchronicity, the use of catalysts, the effect of solvents and salts, etc. On the other hand, as an example, fullerenes (and particularly [60] fullerene) have been found to act as good dienophiles in DA reactions to the extent that many functionalized fullerenes with interesting applications are still synthesized by reacting C60 with dienes. However, despite the very abundant literature about the mechanism of the DA reaction, some pertinent questions have been still pending, including, without being restricted to, the prediction of transition state (TS) geometries and the modeling of DA reactions involving large systems, such as those of C60 fullerene. It must be emphasized that TSs are not easy to predict and the main reason is that many existing algorithms require that the search is initiated from a good starting point (guess TS), which must be very similar to the actual TS. This problem is even more difficult when many TSs are to be located as may be the case in large-scale studies. Moreover, due to the large size of the C60 molecule, the usage of accurate high-level computational methods in the investigation of its reactivity towards dienes is computationally costly, implying the need to find the best threshold between accuracy and computational cost. Therefore, the present study was carried out to contribute to solving the problems of large-scale prediction of DA transition state geometries and the multi-scale modeling of C60 fullerene DA reactions. To address the first problem (large-scale prediction of TSs), we have developed a python program named “AMADAR”, which predicts an unlimited number of DA transition states, using only the SMILES strings of the cycloadducts. AMADAR is customizable and allows for the description of intramolecular DA reactions as well as systems resulting in competing paths. In addition, The AMADAR tool contains two separate modules that perform reaction force analyses and atomic decomposition of energy derivatives from the predicted Intrinsic Reaction Coordinates (IRC) paths. The performance of AMADAR was assessed using 2000 DA cycloadducts and showed a success rate of ~ 95%. Most of the errors were due to basis set inconsistencies or convergence issues that we are still working on. Furthermore, a set of 150 IRC paths generated by the AMADAR program were analyzed to get insight into the (a)synchronicity of DA reactions. This investigation confirmed that the reaction force constant (second derivatives of the system energy with respect to the reaction coordinate) was a good indicator of synchronicity in DA reactions. A close inspection of the profile of has enabled us to propose an alternative classification of DA reactions based on their synchronicity degree, in terms of (quasi)-synchronous, moderate asynchronous, asynchronous, and likely two-steps DA reactions. Natural population analyses seemed to indicate that the global maximum of the reaction force constant could be identified with the formation of all the bonds in the reaction site. Finally, the atomic resolution of energy derivatives suggested that the mechanism of the DA reaction involves two inner elementary processes associated with the formation of each C-C bond. A striking mechanistic difference between synchronous and asynchronous DA reactions emerging from this study is that, in asynchronous reactions, the driving and retarding forces are mainly caused by the fast and slow-forming bonds (elementary process) respectively, while in the case of synchronous ones both elementary processes retard and drive the process concomitantly and equivalently. Regarding the DA reaction of C60 fullerene that was considered to illustrate the problem of multiscale modeling, we have constructed 12 ONIOM2 and 10 ONIOM3 models combining five semi-empirical methods (AM1, PM3, PM3MM, PDDG, PM6) and the LDA(SVWN) functional in conjunction with the B3LYP/6-31G(d) level. Then, their accuracy and efficiency were assessed in comparison with the pure B3LYP/6-31G(d) level considering first the DA reaction between C60 and cyclopentadiene whose experimental data are available. Further, different DFT functionals were employed in place of the B3LYP functional to describe the higher-layer of the best ONIOM partition, and the results obtained were compared to experimental data. At this step, the ONIOM2(M06-2X/6-31 G(d): SVWN/STO-3G) model, where the higher layer encompasses the diene and pyracyclene portion of C60, was found to provide the best tradeoff between accuracy and cost, with respect to experimental data. This model showed errors lower than 2.6 and 2.0 kcal/mol for the estimation of the activation and reaction enthalpies respectively. We have also demonstrated, by comparing several ONIOM2(DFT/6-31G(d): SVWN/STO-3G) models, the importance of dispersion corrections in the accurate estimation of reaction and activation energies. Finally, we have considered a set of 21 dienes, including anthracene, 1,3-butadiene, 1,3-cyclopentadiene, furan, thiophene, selenothiophene, pyrrole and their mono-cyano and hydroxyl derivatives to get insight into the DA reaction of C60 using the best ONIOM2(M06-2X/6-31 G(d): SVWN/STO-3G) model. For a given diene and its derivatives, the analysis of frontier molecular orbitals provides a consistent explanation for the substituent effect on the activation barrier. It revealed that electron-donating (withdrawing) groups such as -OH (–CN) cut down on the activation barrier of the reaction by lowering (extending) of the HOMOdiene – LUMOC60 gap and consequently enhancing (weakening) the interaction between the two reactants. Further, the decomposition of the activation energy into the strain and interaction components suggested that, for a given diene, electron-donating groups (here –OH) diminish the height of the activation barrier not only by favoring the attractive interaction between the diene and C60, but also by reducing the strain energy of the system; the opposite effect is observed for electron-withdrawing groups (here –CN). In contrast with some previous findings on typical DA reactions, we could not infer any general rule applicable to the entire dataset for the prediction of activation energies because the latter does not correlate well with either of the TS polarity, electrophilicity of the diene, or the reaction energy.Thesis (MSc) -- Faculty of Science, Chemistry, 202

Molecular simulations on proteins of biomedical interest : A. Ligand-protein hydration B. Cytochrome P450 2D6 and 2C9 C. Myelin associated glycoprotein (MAG)

TOPIC 1: Water molecules mediating polar interactions in ligand–protein complexes contribute to both binding affinity and specificity. To account for such water molecules in computer-aided drug discovery, we performed an extensive search in the Cambridge Structural Database (CSD) to identify the geometrical criteria defining interactions of water molecules with ligand and protein. In addition, ab initio calculations were used to derive the propensity of ligand hydration. Based on these information we developed an algorithm (AcquaAlta) to reproduce water molecules bridging polar interactions between ligand and protein moieties. This approach was validated using 20 crystal structures and yielded a match of 76% between experimental and calculated water positions. The solvation algorithm was then applied to the docking of oligopeptides to the periplasmic oligopeptide binding protein A (OppA), supported by a pharmacophore-based alignment tool. TOPIC 2: Drug metabolism, toxicity, and interaction profile are major issues in the drug discovery and lead optimization processes. The Cytochromes P450 (CYPs) 2D6 and 2C9 are enzymes involved in the oxidative metabolism of a majority of the marketed drugs. By identifying the binding mode using pharmacophore pre-alignement and automated flexible docking, and quantifying the binding affinity by multi-dimensional QSAR, we validated a model family of 56 compounds (46 training, 10 test) and 85 (68 training, 17 test) for CYP2D6 and CYP2C9, respectively. The correlation with the experimental data (cross- validated r2 = 0.811 for CYP2D6 and 0.687 for CYP2C9) suggests that our approach is suited for predicting the binding affinity of compounds towards the CYP2D6 and CYP2C9. The models were challenged by Y-scrambling, and by testing an external dataset of binding compounds (15 compounds for CYP2D6 and 40 for CYP2C9) and not binding compounds (64 compounds for CYP2D6 and 56 for CYP2C9). TOPIC 3: After injury, neurites from mammalian adult central nervous systems are inhibited to regenerate by inhibitory proteins such as the myelin-associated glycoprotein (MAG). The block of MAG with potent glycomimetic antagonists could be a fruitful approach to enhance axon regeneration. Libraries of MAG antagonists were derived and synthesized starting from the (general) sialic acid moiety. The binding data were rationalized by docking studies, molecular dynamics simulations and free energy perturbations on a homology model of MAG. The pharmacokinetic profile (i.e. stability in cerebrospinal fluid, logD, and blood-brain barrier permeation) of these compounds has been thoroughly investigated to evaluate the drug-likeness of the identified antagonists