Quantum-Chemical Quasi-Docking for Molecular Dynamics Calculations

The quantum quasi-docking procedure is used to compare the docking accuracies of two quantum-chemical semiempirical methods, namely, PM6-D3H4X and PM7. Quantum quasi-docking is an approximation to quantum docking. In quantum docking, it is necessary to search directly for the global minimum of the energy of the protein-ligand complex calculated by the quantum-chemical method. In quantum quasi-docking, firstly, we look for a wide spectrum of low-energy minima, calculated using the MMFF94 force field, and secondly, we recalculate the energies of all these minima using the quantum-chemical method, and among these recalculated energies we determine the lowest energy and the corresponding ligand position. Both PM6-D3H4X and PM7 are novel methods that describe well-dispersion interactions, hydrogen and halogen bonds. The PM6-D3H4X and PM7 methods are used with the COSMO implicit solvent model as it is implemented in the MOPAC program. The comparison is made for 25 high quality protein-ligand complexes. Firstly, the docking positioning accuracies have been compared, and we demonstrated that PM7+COSMO provides better positioning accuracy than PM6-D3H4X. Secondly, we found that PM7+COSMO demonstrates a much higher correlation between the calculated and measured protein–ligand binding enthalpies than PM6-D3H4X. For future quantum docking PM7+COSMO is preferable, but the COSMO model must be improved.

Fast and automated identification of reactions with low barriers: the decomposition of 3-hydroperoxypropanal

We show how fast semiempirical QM methods can be used to significantly decrease the computational expense for automated reaction mechanism discovery, using two different method for generating reaction products: graph-based systematic enumeration of all possible products and the meta-dynamics approach by Grimme (J. Chem. Theory. Comput. 2019, 15, 2847). We test the two approaches on the low-barrier reactions of 3-hydroperoxypropanal, which have been studied by a large variety of reaction discovery approaches and therefore provides a good benchmark. By using PM3 and GFN2-xTB for reaction energy and barrier screening the systematic approach identifies 64 reactions (out of 27,577 possible reactions) for DFT refinement, which in turn identifies the three reactions with lowest barriers plus a previously undiscovered reaction. With optimized hyperparameters meta-dynamics followed by PM3/GFN2-xTB-based screening identifies 15 reactions for DFT refinement, which in turn identifies the three reactions with lowest barrier. The number of DFT refinements can be further reduced to as little as six for both approaches by first verifying the transition states with GFN1-xTB. The main conclusion is that the semiempirical methods are accurate and fast enough to automatically identify promising candidates for DFT refinement for the low barrier reactions of 3-hydroperoxypropanal in about 15-30 minutes using relatively modest computational resources.

Structural Imperfections of the Hydroxyapatite Surface Layer to Engineer Its Electrical Potential

The Doctoral Thesis aims to identify the influence of hydroxyapatite (Hap) defects (such as OH-group, H-, O-vacancies, H-interstitials, and their combination) on the electrical potential of HAp’s surface which influences biocompatibility and control cell adhesion. HAp contains various structural imperfections (defects) and has a non-stoichiometric composition. The structural imperfections induce the heterogeneity of the surface electrical potential. However, the role of the defects OH-group, H-, O-vacancies, H-interstitials, and their combination in the formation of HAp surface polarization and their influence on HAp surface charge, energy band structure and electron work function has not yet been investigated. In this Thesis, for the first time the theoretical and experimental approaches were used to investigate the influence of structural imperfections (OH-, H-, O-vacancies, H-interstitials, and hydrogen atoms filling unsaturated hydrogen bonds) on HAp electrical properties. The computer simulations of HAp structures analyse the properties of these effects. Semiempirical methods of molecular mechanics and quantum mechanics, as well as methods of density functional theory were employed. The experimental studies of the HAp surface electrical properties were carried out by photoluminescence (PL) emission, synchrotron excitation spectroscopy, threshold photoelectron (PE) emission spectroscopy. The influence of annealing, hydrogenation, microwave, gamma irradiation and their combination on HAp defects was investigated experimentally for the first time. The obtained results are in accordance with computational data. The achieved results will help to improve technologies to engineer the surface charge of Hap.

Fast and Automated Identification of Reactions with Low Barriers: The Decomposition of 3-Hydroperoxypropanal

We show how fast semiempirical QM methods can be used to significantly decrease the CPU requirements for automated reaction mechanism discovery, using two different method for generating reaction products: graph-based systematic enumeration of all possible products and the meta-dynamics approach by Grimme (J. Chem. Theory. Comput. 2019, 15, 2847). We test the two approaches on the low-barrier reactions of 3-hydroperoxypropanal, which have been studied by a large variety of reaction discovery approaches and therefore provides a good benchmark. By using PM3 and GFN2-xTB for reaction energy and barrier screening the systematic approach identifies 64 reactions (out of 27,577 possible reactions) for DFT refinement, which in turn identifies the three reactions with lowest barriers plus a previously undiscovered reaction. With optimised hyperparameters meta-dynamics followed by PM3/GFN2-xTB-based screening identifies 15 reactions for DFT refinement, which in turn identifies the three reactions with lowest barrier. The number of DFT refinements can be further reduced to as little as six for both approaches by first verifying the transition states with GFN1-xTB. The main conclusion is that the semiempirical methods are accurate and fast enough to automatically identify promising candidates for DFT refinement for the low barrier reactions of 3-hydroperoxypropanal in about 15-30 minutes using relatively modest computational resources.

A Robust and Efficient Implicit Solvation Model for Fast Semiempirical Methods

We present a robust and efficient method to implicitly account for solvation effects in modern semiempirical quantum mechanics and force-fields. A computationally efficient yet accurate solvation model based on the analytical linearized Poisson--Boltzmann~(ALPB) model is parameterized for the extended tight binding (xTB) and density functional tight binding (DFTB) methods as well as for the recently proposed GFN-FF general force-field. The proposed methods perform well over a broad range of systems and applications, from conformational energies over transition-metal complexes to large supramolecular association reactions of charged species. For hydration free energies of small molecules GFN1-xTB(ALPB) is reaching the accuracy of sophisticated explicitly solvated approaches, with a mean absolute deviation of only 1.4 kcal/mol compared to experiment. Logarithmic octanol--water partition coefficients (log Kow) are computed with a mean absolute deviation of about 0.65 using GFN2-xTB(ALPB) compared to experimental values indicating a consistent description of differential solvent effects. Overall, more than twenty solvents for each of the six semiempirical methods are parameterized and tested. They are readily available in the xtb and dftb+ programs for diverse computational applications.

Electrophilic Aromatic Substitution: theoretical insight into halonium ion self-existence in catalytic system

Potential Energy Scan (PES) has already proven to be a powerful tool in computational chemistry to detect critical points in the energy path of a system, such as transition states and local minima/maxima in energy convergence. Previous studies showed a wide application of PES in many different fields of physical-chemical sciences, such as materials, supramolecular, and catalysis chemistry. Moreover, the evaluation of the basic PES algorithms at a reasonably affordable level of theory has in principle revealed good basic statistical relationships that allow further investigations in this research area. Herein, a simple and fast graphical method for accurate PES evaluation was proposed, performed at the PM7 semiempirical level of theory for catalytic systems in electrophilic aromatic substitution processes. The results presented in this case study showed a relative error ranging from 1.5 to 27.1% for most catalytic-electrophiloid systems. The treatment of such systems with PES algorithms led to novel iron(V) species and opened a completely new field in tandem transition metal-nonmetal catalysis, implying entirely new insights. Moreover, the basic statistical analysis showed that there are no significant outliers, and therefore it can be concluded that the graphical analysis approach can be used in further detailed treatment of PES results in the search for saddle points and prediction of transition state properties under known conditions in the DFT and MP2 functions discussed here. The novel graphical methodology has been introduced by two applied graphical methods, and its accuracy demonstrated in semiempirical methods provides solid results in view of future development and application in a wide range of chemical sciences.

Fast and Automated Identification of Reactions with Low Barriers: The Decomposition of 3-Hydroperoxypropanal

<div> <div> <div> <p>We show how fast semiempirical QM methods can be used to significantly decrease the CPU requirements for automated reaction mechanism discovery, using two different method for generating reaction products: graph-based systematic enumeration of all possible products and the meta-dynamics approach by Grimme (<i>J. Chem. Theory. Comput</i>. 2019, 15, 2847). We test the two approaches on the low-barrier reactions of 3-hydroperoxypropanal, which have been studied by a large variety of reaction discovery approaches and therefore provides a good benchmark. By using PM3 and GFN2-xTB for reaction energy and barrier screening the systematic approach identifies 64 reactions (out of 27,577 possible reactions) for DFT refinement, which in turn identifies the three reactions with lowest barriers plus a previously undiscovered reaction. With optimised hyperparameters meta-dynamics followed by PM3/GFN2-xTB-based screening identifies 15 reactions for DFT refinement, which in turn identifies the three reactions with lowest barrier. The number of DFT refinements can be further reduced to as little as six for both approaches by first verifying the transition states with GFN1-xTB. The main conclusion is that the semiempirical methods are accurate and fast enough to automatically identify promising candidates for DFT refinement for the low barrier reactions of 3-hydroperoxypropanal in a few hours using modest computational resources. </p> </div> </div> </div>

Fast and Automated Identification of Reactions with Low Barriers: The Decomposition of 3-Hydroperoxypropanal

<div> <div> <div> <p>We show how fast semiempirical QM methods can be used to significantly decrease the CPU requirements for automated reaction mechanism discovery, using two different method for generating reaction products: graph-based systematic enumeration of all possible products and the meta-dynamics approach by Grimme (<i>J. Chem. Theory. Comput</i>. 2019, 15, 2847). We test the two approaches on the low-barrier reactions of 3-hydroperoxypropanal, which have been studied by a large variety of reaction discovery approaches and therefore provides a good benchmark. By using PM3 and GFN2-xTB for reaction energy and barrier screening the systematic approach identifies 64 reactions (out of 27,577 possible reactions) for DFT refinement, which in turn identifies the three reactions with lowest barriers plus a previously undiscovered reaction. With optimised hyperparameters meta-dynamics followed by PM3/GFN2-xTB-based screening identifies 15 reactions for DFT refinement, which in turn identifies the three reactions with lowest barrier. The number of DFT refinements can be further reduced to as little as six for both approaches by first verifying the transition states with GFN1-xTB. The main conclusion is that the semiempirical methods are accurate and fast enough to automatically identify promising candidates for DFT refinement for the low barrier reactions of 3-hydroperoxypropanal in a few hours using modest computational resources. </p> </div> </div> </div>

Evaluation of Thermochemical Machine Learning for Potential Energy Curves and Geometry Optimization

While many machine learning methods, particularly deep neural networks have been trained for density functional and quantum chemical energies and properties, the vast majority of these methods focus on single-point energies. In principle, such ML methods, once trained, offer thermochemical accuracy on par with density functional and wave function methods but at speeds comparable to traditional force fields or approximate semiempirical methods. So far, most efforts have focused on optimized equilibrium single-point energies and properties. In this work, we evaluate the accuracy of several leading ML methods across a range of bond potential energy curves and torsional potentials. Methods were trained on the existing ANI-1 training set, calculated using the ωB97X / 6-31G(d) single points at non-equilibrium geometries. We find that across a range of small molecules, several methods offer both qualitative accuracy (e.g., correct minima, both repulsive and attractive bond regions, anharmonic shape, and single minima) and quantitative accuracy in terms of the mean absolute percent error near the minima. At the moment, ANI-2x, FCHL, and our new grid-based convolutional neural net show good performance.