Semiempirical Quantum Mechanical Methods Research Articles

MHCBI: a pipeline for calculating peptide-MHC binding energy using semi-empirical quantum mechanical methods with explicit/implicit solvent models.

Experimentally estimating peptide-major histocompatibility complex (pMHC) binding affinity has been quite challenging due to the many receptors and the many potential ligands implicated in it. We have thus proposed a straightforward computational methodology considering the different mechanisms involved in pMHC binding to facilitate studying such receptor-ligand interactions. We have developed a pipeline using semi-empirical quantum mechanical methods for calculating pMHC class I and II molecules' binding energy (BE). This pipeline can systematize the methodology for calculating pMHC system BE, enabling the rational design of T-cell epitopes to be used as pharmaceuticals and vaccines.

Electrical Conductance And Theoretical Study Of Glutamic Acid In Different Solvents at 310.16k

The electrical conductivities of glutamic acid in the water, methanol, and ethanol are measured at 310.16 K and the conductivity parameters: Association constant (KA), equivalent conductance at infinite dilution (Λo) and the distance parameter (R). (Λo, KA, and R ) are calculated. Values of Λo are found to be in the order: water > methanol > ethanol , but the order is reversed for the amount of KA and R. This indicates the increase of ion-solvent interaction and formation of solvent separated ion-pair in the above order. The main interest is to find an accurate yet efficient solvation model for semiempirical quantum-mechanical and Density Function Theory (DFT) methods applicable to amino acids (glutamic acid) in the context of computer-aided conductivity studying. It was reparametrization of the Conductor like Screening Model (COSMO) and Conductor-like Polarizable Continuum Model (CPCM) CPCM solvent model for Parameterization method three (PM3), Hartree- Fock (HF) & DFT calculation in Gaussian interface, version 16.0 which used to calculate many descriptors of the glutamic molecule and study the relationship between these descriptors and the association constant (KA). Molecular volume (MV), Connolly parameters, and the entropy were showed the same result corresponding with the experimental parameters..

Non-Covalent Interactions Atlas Benchmark Data Sets 3: Repulsive Contacts.

The new R739×5 data set from the Non-Covalent Interactions Atlas series (www.nciatlas.org) focuses on repulsive contacts in molecular complexes, covering organic molecules, sulfur, phosphorus, halogens, and noble gases. Information on the repulsive parts of the potential energy surface is crucial for the development of robust empirically parametrized computational methods. We use the new data set of highly accurate CCSD(T)/CBS interaction energies to test selected density functional theory (DFT) and semiempirical quantum-mechanical methods. The double-hybrid functionals were the best performing, with the revDSD-PBEP86-D3 being the most accurate DFT method, followed by the range-separated ωB97X functionals. Out of semiempirical methods, GFN2-xTB yielded the best results. On the example of the PM6 method, we analyze the source of error and its relation to the difficulties in the description of conformational energies, and we also devise an immediately applicable correction that fixes the most serious uncorrected issues previously encountered in practical calculations.

Quantification of Noncovalent Interactions in Azide-Pnictogen, -Chalcogen, and -Halogen Contacts.

The noncovalent interactions between azides and oxygen‐containing moieties are investigated through a computational study based on experimental findings. The targeted synthesis of organic compounds with close intramolecular azide–oxygen contacts yielded six new representatives, for which X‐ray structures were determined. Two of those compounds were investigated with respect to their potential conformations in the gas phase and a possible significantly shorter azide–oxygen contact. Furthermore, a set of 44 high‐quality, gas‐phase computational model systems with intermolecular azide–pnictogen (N, P, As, Sb), –chalcogen (O, S, Se, Te), and –halogen (F, Cl, Br, I) contacts are compiled and investigated through semiempirical quantum mechanical methods, density functional approximations, and wave function theory. A local energy decomposition (LED) analysis is applied to study the nature of the noncovalent interaction. The special role of electrostatic and London dispersion interactions is discussed in detail. London dispersion is identified as a dominant factor of the azide–donor interaction with mean London dispersion energy‐interaction energy ratios of 1.3. Electrostatic contributions enhance the azide–donor coordination motif. The association energies range from −1.00 to −5.5 kcal mol−1.

Single-Point Hessian Calculations for Improved Vibrational Frequencies and Rigid-Rotor-Harmonic-Oscillator Thermodynamics.

The calculation of harmonic vibrational frequencies (HVF) to interpret infrared (IR) spectra and to convert molecular energies to free energies is one of the essential steps in computational chemistry. A prerequisite for accurate thermostatistics so far was to optimize the molecular input structures in order to avoid imaginary frequencies, which inevitably leads to changes in the geometry if different theoretical levels are applied for geometry optimization and frequency calculations. In this work, we propose a new method termed single-point Hessian (SPH) for the computation of HVF and thermodynamic contributions to the free energy within the modified rigid-rotor-harmonic-oscillator approximation for general nonequilibrium molecular geometries. The key ingredient is the application of a biasing potential given as Gaussian functions expressed with the root-mean-square-deviation (RMSD) in Cartesian space in order to retain the initial geometry. The theory derived herein is generally applicable to quantum mechanical (QM), semiempirical QM, and force-field (FF) methods. Besides a detailed description of the underlying theory including the important back-correction of the biased HVF, the SPH approach is tested for reaction paths, molecular dynamics snapshots of crambin, and supramolecular association free energies in comparison to high-level density functional theory (DFT) values. Furthermore, the effect on IR spectra is investigated for organic dimers and transition-metal complexes revealing improved spectra at low theoretical levels. On average, DFT reference free energies are better reproduced by the newly developed SPH scheme than by conventional calculations on freely optimized geometries or without any relaxation.

In silico study of medicinal plants with cyclodextrin inclusion complex as the potential inhibitors against SARS-CoV-2 main protease (Mpro) and spike (S) receptor.

The current outbreak of novel coronavirus disease (COVID-19) causes an alarming number of deaths in 221 countries around the world. Nowadays, there is no specific and effective drug regimen for curing COVID-19. Since the COVID-19 pandemic, several medicinal plants with promising results in the previous SARS-CoV could be used to treat SARS-CoV-2 infected patients. This work assesses proven medicinal plants as potential inhibitors against SARS-CoV-2 main protease (Mpro) and spike (S) receptors by employing in silico methods. Molecular docking studies and 3D structure-based pharmacophore modeling were performed to identify the molecular interactions of potential active molecules with the Mpro and (S) receptor of SARS-CoV-2. The drug-likeness and ADME properties were also predicted to support the drug-like nature of the selected active molecules. The results indicated that the most favorable ligand was Terrestriamide with (ΔG: ─8.70 kcal/mol; Ki: 0.417 μM) and (ΔG: ─7.02 kcal/mol; Ki: 7.21 μM) for Mpro and (S) receptor, respectively. Terrestriamide is also supported with a high drug-likeness value and appropriate ADME profile. Furthermore, to improve drug delivery, the cyclodextrin inclusion complex was calculated based on semi-empirical quantum mechanical methods. Terrestriamide/γ−cyclodextrin is the most favorable pathway of inclusion complex formation and could be used to treat COVID-19.

COMPUTER MODELLING OF A VERY HEAT RESISTANT Hf6C3N2 FOR COVER OF HYPERSONIC FLIGHT VEHICLES

The creation of a very heat resistant cover for returnable orbiters and for hypersonic flying aircrafts is an actual problem in modern science and technics. Therefore, computer modelling and investigations molecular structure of various multicomponent compounds are important tasks. In the paper, based on the computer model the molecular structure and a very heat resistant of carbonitride hafnium Hf6C3N2 have been investigated by using of the semiempirical quantum mechanical method PM3. Some mechanical, electrical, optical and thermodynamic parameters of this material have been researched. Debye temperature and melting temperature have been calculated. The found theoretical value of melting temperature of carbonitride hafnium Hf6C3N2 is approximated to obtained experimental results. The block diagram of carried out investigations has been presented. The results of investigations indicate that these materials can be applied as a very heat resistant cover for hypersonic flight vehicles and returnable orbiter.

Comprehensive Assessment of GFN Tight-Binding and Composite Density Functional Theory Methods for Calculating Gas-Phase Infrared Spectra.

Vibrational spectroscopy is a valuable and widely used analytical tool for the characterization of chemical substances. We investigate the performance of semiempirical quantum mechanical GFN tight-binding and force-field methods for the calculation of gas-phase infrared spectra in comparison to experiment and low-cost (B3LYP-3c) density functional theory. A data set of 7247 experimental references was used to evaluate method performance based on automatic spectra comparison. Various quantitative spectral similarity measures were employed for the comparison between theory and experiment and for determining empirical scaling factors. It is shown that the scaling of atomic masses provides an accurate yet simple alternative to standard global frequency scaling in density functional theory (DFT) and semiempirical calculations. Furthermore, the method performance for 58 exemplary transition metal complexes was investigated. The efficient DFT composite method B3LYP-3c, that was introduced in the course of this work, was found to be excellently suited for general IR spectra calculations. The GFN1- and GFN2-xTB tight-binding methods clearly outperformed the PMx competitors. Conformational changes were investigated for a subset of the data and are found to have a mediocre strong influence on the simulated spectra suggesting that the corresponding elaborate sampling steps may be neglected in automated compound identification workflows.

Computational Study on the Electronic Properties of Graphene/Calcium Oxide Nanocomposite

Semiempirical PM6 quantum mechanical method was used to investigate the changes in the electronic and physical parameters of graphene before and after interaction with calcium oxide (CaO). Calculations were conducted the optimized structures with graphene interacting with two CaO molecules forming G/CaO nanocomposite once through the O atom then through the Ca atom, in the form of adsorb and complex states. Results indicated the deformation of graphene structure upon interaction with CaO but the resulting G/CaO is more thermally stable than graphene itself. The studied G/CaO composites were also found to be more reactive than graphene in regard to their higher calculated total dipole moments. The calculated thermal parameters further confirmed the thermal stability of G/CaO composites.

Benchmark Study of Electrochemical Redox Potentials Calculated with Semiempirical and DFT Methods

The calculation of redox potentials by semiempirical quantum mechanical (SQM) approaches is evaluated with a focus on the recently developed GFNn-xTB methods. The assessment is based on a data set comprising 313 experimental redox potentials of small to medium-sized organic and organometallic molecules in various solvents. This compilation is termed as ROP313 (reduction and oxidation potentials 313) and divided for analysis purposes into the organic subset OROP and the organometallic subset OMROP. Corresponding data for a few common density functional theory (DFT) functionals employing extended AO basis sets and small basis-set DFT composite schemes are computed for comparison. Continuum solvation models are used to calculate the important solvation free energy contribution. The results for ROP313 show that the GFNn-xTB methods provide a robust, efficient, and generally applicable workflow for the routine calculation of redox potentials. The GFNn-xTB methods outperform the PMx competitor for the OROP subset (mean absolute deviation from the experiment, MADGFN2-xTB = 0.30 V, MADGFN1-xTB = 0.31 V, PM6-D3H4 = 0.61 V, PM7 = 0.60 V), almost reaching low-cost DFT quality (MADB97-3c = 0.25 V) at drastically reduced computational cost (2-3 orders of magnitude). All SQM methods perform considerably worse for the OMROP subset. Here, the GFN2-xTB still yields semiquantitative results slightly better and more robustly than with the PMx methods (MADGFN2-xTB = 0.74 V, PM6-D3H4 = 0.78 V, PM7 = 0.82 V). The proposed workflow enables large-scale quantum chemical computations of organic and, to a lesser extent, organometallic molecule redox potentials on common laptop computers in seconds to minutes of computation time enabling, e.g., screening of extended compound libraries.

Graphics Processing Unit-Accelerated Semiempirical Born Oppenheimer Molecular Dynamics Using PyTorch.

A new open-source high-performance implementation of Born Oppenheimer molecular dynamics based on semiempirical quantum mechanics models using PyTorch called PYSEQM is presented. PYSEQM was designed to provide researchers in computational chemistry with an open-source, efficient, scalable, and stable quantum-based molecular dynamics engine. In particular, PYSEQM enables computation on modern graphics processing unit hardware and, through the use of automatic differentiation, supplies interfaces for model parameterization with machine learning techniques to perform multiobjective training and prediction. The implemented semiempirical quantum mechanical methods (MNDO, AM1, and PM3) are described. Additional algorithms include a recursive Fermi-operator expansion scheme (SP2) and extended Lagrangian Born Oppenheimer molecular dynamics allowing for rapid simulations. Finally, benchmark testing on the nanostar dendrimer and a series of polyethylene molecules provides a baseline of code efficiency, time cost, and scaling and stability of energy conservation, verifying that PYSEQM provides fast and accurate computations.

Using the Semiempirical Quantum Mechanics in Improving the Molecular Docking: A Case Study with CDK2.

In this study, we use some modified semiempirical quantum mechanics (SQM) methods for improving the molecular docking process. To this end, the three popular SQM Hamiltonians, PM6, PM6-D3H4X, and PM7 are employed for geometry optimization of some binding modes of ligands docked into the human cyclin-dependent kinase 2 (CDK2) by two widely used docking tools, AutoDock and AutoDock Vina. The results were analyzed with two different evaluation metrics: the symmetry-corrected heavy-atom RMSD and the fraction of recovered ligand-protein contacts. It is shown that the evaluation of the fraction of recovered contacts is more useful to measure the similarity between two structures when interacting with a protein. It was also found that AutoDock is more successful than AutoDock Vina in producing the correct ligand poses (RMSD≤2.0 Å) and ranking of the poses. It is also demonstrated that the ligand optimization at the SQM level improves the docking results and the SQM structures have a significantly better fit to the observed crystal structures. Finally, the SQM optimizations reduce the number of close contacts in the docking poses and successfully remove most of the clash or bad contacts between ligand and protein.

Interaction Nature and Computational Methods for Halogen Bonding: A Perspective.

Halogen bonds are noncovalent interactions that have been widely used in many fields, including drug design, crystal engineering, and material sciences. A clear understanding of the nature of halogen bonding as well as the proper theoretical bonding description, especially the development of efficient and accurate computational chemical methods and their application in complex systems, is of great significance to promote the development of related fields. In this perspective, we reviewed the investigations of the nature of halogen bonding in recent years and discussed the development of quantum mechanical, molecular mechanical, and empirical scoring function methods in properly describing halogen-bonding interactions, as well as their achievements in corresponding areas. An evaluation on the performance of various quantum mechanical and semiempirical quantum mechanical methods in describing halogen bonds was also included, involving the DFT-D4 scheme and the recently reported xTB methods. We hope this perspective may be helpful, from the insights of computational tools and methods, in providing reference and enlightenment for the application of halogen bonds in fields like high-throughput virtual screening and rational drug design.

Predictive Model of Charge Mobilities in Organic Semiconductor Small Molecules with Force-Matched Potentials.

Charge mobility of crystalline organic semiconductors (OSC) is limited by local dynamic disorder. Recently, the charge mobility for several high mobility OSCs, including TIPS-pentacene, were accurately predicted from a density functional theory (DFT) simulation constrained by the crystal structure and the inelastic neutron scattering spectrum, which provide direct measures of the structure and the dynamic disorder in the length scale and energy range of interest. However, the computational expense required for calculating all of the atomic and molecular forces is prohibitive. Here we demonstrate the use of density functional tight binding (DFTB), a semiempirical quantum mechanical method that is 2 to 3 orders of magnitude more efficient than DFT. We show that force matching a many-body interaction potential to DFT derived forces yields highly accurate DFTB models capable of reproducing the low-frequency intricacies of experimental inelastic neutron scattering (INS) spectra and accurately predicting charge mobility. We subsequently predicted charge mobilities from our DFTB model of a number of previously unstudied structural analogues to TIPS-pentacene using dynamic disorder from DFTB and transient localization theory. The approach we establish here could provide a truly rapid simulation pathway for accurate materials properties prediction, in our vision applied to new OSCs with tailored properties.

High-Throughput Docking Using Quantum Mechanical Scoring.

Today high-throughput docking is one of the most commonly used computational tools in drug lead discovery. While there has been an impressive methodological improvement in docking accuracy, docking scoring still remains an open challenge. Most docking programs are rooted in classical molecular mechanics. However, to better characterize protein-ligand interactions, the use of a more accurate quantum mechanical (QM) description would be necessary. In this work, we introduce a QM-based docking scoring function for high-throughput docking and evaluate it on 10 protein systems belonging to diverse protein families, and with different binding site characteristics. Outstanding results were obtained, with our QM scoring function displaying much higher enrichment (screening power) than a traditional docking method. It is acknowledged that developments in quantum mechanics theory, algorithms and computer hardware throughout the upcoming years will allow semi-empirical (or low-cost) quantum mechanical methods to slowly replace force-field calculations. It is thus urgently needed to develop and validate novel quantum mechanical-based scoring functions for high-throughput docking toward more accurate methods for the identification and optimization of modulators of pharmaceutically relevant targets.

Accuracy of the PM6 and PM7 Methods on Bare and Thiolate-Protected Gold Nanoclusters

Semiempirical quantum mechanical (SEQM) methods offer an attractive middle ground between fully ab initio quantum chemistry and force-field simulations, allowing for a quantum mechanical treatment of the system at a relatively low computational cost. However, SEQM methods have not been frequently utilized in the study of transition metal systems, mostly due to the difficulty in obtaining reliable parameters. This paper examines the accuracy of the PM6 and PM7 semiempirical methods to predict geometries, ionization potentials, and HOMO-LUMO energy gaps of several bare gold clusters (Aun) and thiolate-protected gold nanoclusters (AuSNCs). Contrary to PM6, the PM7 method can predict qualitatively correct geometries and ionization potentials when compared to DFT. PM6 fails to predict the characteristic gold core and gold-sulfur ligand shell (staple motifs) of the AuSNC structures. Both the PM6 and PM7 methods overestimate the HOMO-LUMO gaps. Overall, PM7 provides a more accurate description of bare gold and gold-thiolate nanoclusters than PM6. Nevertheless, refining the gold parameters could help achieve better quantitative accuracy.

Non-Covalent Interactions Atlas Benchmark Data Sets: Hydrogen Bonding.

The Non-Covalent Interactions Atlas project (www.nciatlas.org) aims to cover a wide range of noncovalent interactions with a new generation of benchmark data sets. This Article presents the first two data sets focused on hydrogen bonding: HB375, featuring neutral systems, and IHB100 for ionic H-bonds. Both data sets are complemented by 10-point dissociation curves (HB375×10, IHB100×10). The interaction energies are extrapolated to the CCSD(T)/CBS limit from calculations in large basis sets. The Article also summarizes the design principles that will be used to construct the subsequent data sets in the series. The testing of DFT-D methods on the HB375 set has revealed interesting, previously unnoticed issues. The application of the new data to the testing and parametrization of semiempirical QM methods is also discussed.

Quantitative Structure Activity Relationship Analysis of 1,3,4-Thiadiazole Derivatives as Anti-Inflamatory using Parameterized Model 3 Method

Quantitative Relationship Structure and Activity Relationship (QSAR) studies conducted on the anti-inflammatory activity of a series of 1,3,4-thiadiazole derivatives which aim to obtain an equation to predict the value of the anti-inflammatory activity of. As research material was experimental biological activity data of 28 1,3,4-thiadiazole derivatives which were divided into 25 fitting compounds and 3 test compounds. QSAR analysis was carried out based on multiple linear regression calculations of fitting compounds by plotting log BA as the dependent variable and the independent variable was the net charge of carbon and nitrogen atoms bound to the dressing group, dipole moment (µ), HOMO-LUMO, Log P, molecular weight, polarizability, hydration energy, and van der waals volume. The value of descriptors was obtained from calculations using the PM3 semi-empirical quantum mechanical method. The result of QSAR equation was:Log BA = 68.112 − 8.482 (qC1) + 14.764 (qC15) + 1.071 (Log P) − 0.018 (MW) − 0.484 (µ) − 4.427E-5 (Polarizability) − 0.561 (E. Hydration) + 7.843 (HOMO) − 1.489 (LUMO)n = 28, r = 0.861, SE = 0.170098, Fcalculate / Ftable = 2.02116, PRESS = 1.9665

Benchmarking of Semiempirical Quantum-Mechanical Methods on Systems Relevant to Computer-Aided Drug Design.

The semiempirical quantum mechanical (SQM) methods used in drug design are commonly parametrized and tested on data sets of systems that may not be representative models for drug-biomolecule interactions in terms of both size and chemical composition. This is addressed here with a new benchmark data set, PLF547, derived from protein-ligand complexes, consisting of complexes of ligands with protein fragments (such as amino-acid side chains), with interaction energies based on MP2-F12 and DLPNO-CCSD(T) calculations. From these, composite benchmark interaction energies are also built for complexes of the ligand with the complete active site of the protein (PLA15 data set). These data sets are used to test multiple SQM methods with corrections for noncovalent interactions; the role of the solvation model in the calculations is tested as well.

What Next for Quantum Mechanics in Structure-Based Drug Discovery?

There is significant potential for electronic structure methods to improve the quality of the predictions furnished by the tools of computer-aided drug design, which typically rely on empirically derived functions. In this perspective, we consider some recent examples of how quantum mechanics has been applied in predicting protein-ligand geometries, protein-ligand binding affinities and ligand strain on binding. We then outline several significant developments in quantum mechanics methodology likely to influence these approaches: in particular, we note the advent of more computationally expedient ab initio quantum mechanical methods that can provide chemical accuracy for larger molecular systems than hitherto possible. We highlight the emergence of increasingly accurate semiempirical quantum mechanical methods and the associated role of machine learning and molecular databases in their development. Indeed, the convergence of improved algorithms for solving and analyzing electronic structure, modern machine learning methods, and increasingly comprehensive benchmark data sets of molecular geometries and energies provides a context in which the potential of quantum mechanics will be increasingly realized in driving future developments and applications in structure-based drug discovery.