On the performance of the semiempirical quantum mechanical PM6 and PM7 methods for noncovalent interactions

PointGAT: A Quantum Chemical Property Prediction Model Integrating Graph Attention and 3D Geometry

Predicting quantum chemical properties is a fundamental challenge for computational chemistry. While the development of graph neural networks has advanced molecular representation learning and property prediction, their performance could be further enhanced by incorporating three-dimensional (3D) structural geometry into two-dimensional (2D) molecular graph representation. In this study, we introduce the PointGAT model for quantum molecular property prediction, which integrates 3D molecular coordinates with graph-attention modeling. Comparison with other current models in molecular prediction tasks showed that PointGAT could provide higher predictive accuracy in various benchmark data sets from MoleculeNet, including ESOL, FreeSolv, Lipop, HIV, and 6 out of 12 tasks of the QM9 data set. To further examine PointGAT prediction of quantum mechanical (QM) energies, we constructed a C10 data set comprising 11,841 charged and chiral carbocation intermediates with QM energies calculated at the DM21/6-31G*//B3LYP/6-31G* levels. Notably, PointGAT achieved an R2 value of 0.950 and an MAE of 1.616 kcal/mol, outperforming even the best-performing graph neural network model with a reduction of 0.216 kcal/mol in MAE and an improvement of 0.050 in R2. Additional ablation studies indicated that incorporating molecular geometry into the model resulted in markedly higher predictive accuracy, reducing the MAE value from 1.802 to 1.616 kcal/mol. Moreover, visualization of PointGAT atomic attention weights suggested its predictions were interpretable. Findings in this study support the application of PointGAT as a powerful and versatile tool for quantum chemical property prediction that can facilitate high-accuracy modeling for fundamental exploration of chemical space as well as drug design and molecular engineering.

Structural variety and pharmacological potential of naphthylisoquinoline alkaloids

Naphthylisoquinoline alkaloids are a fascinating class of natural biaryl compounds. They show characteristic mono- and dimeric scaffolds, with chiral axes and stereogenic centers. Since the appearance of the last comprehensive overview on these secondary plant metabolites in this series in 1995, the number of discovered representatives has tremendously increased to more than 280 examples known today. Many novel-type compounds have meanwhile been discovered, among them naphthylisoquinoline-related follow-up products like e.g., the first seco-type (i.e., ring-opened) and ring-contracted analogues. As highlighted in this review, the knowledge on the broad structural chemodiversity of naphthylisoquinoline alkaloids has been decisively driven forward by extensive phytochemical studies on the metabolite pattern of Ancistrocladus abbreviatus from Coastal West Africa, which is a particularly "creative" plant. These investigations furnished a considerable number of more than 80-mostly new-natural products from this single species, with promising antiplasmodial activities and with pronounced cytotoxic effects against human leukemia, pancreatic, cervical, and breast cancer cells. Another unique feature of naphthylisoquinoline alkaloids is their unprecedented biosynthetic origin from polyketidic precursors and not, as usual for isoquinoline alkaloids, from aromatic amino acids-a striking example of biosynthetic convergence in nature. Furthermore, remarkable botanical results are presented on the natural producers of naphthylisoquinoline alkaloids, the paleotropical Dioncophyllaceae and Ancistrocladaceae lianas, including first investigations on the chemoecological role of these plant metabolites and their storage and accumulation in particular plant organs.

Do electrostatic interactions make a difference in physics-based AutoDock4 scoring function?

Physics-based scoring function AutoDock4 is one of the most successfully applied tools in the area of structure-based drug design. However, current scoring functions are still far from being perfect. In a recent work highlighting the strengths and deficiencies of current scoring functions, we discovered that the residual error of ΔGbind predictions made by AutoDock4 is highly correlated to the presence of formally charged fragments in a ligand. In this work, we study how the use of the high-quality atomic charges, applied for contemporary force fields calculation, affects the quality of the experimental ΔGbind prediction by means of AutoDock4. We initially expected that the previously found discrepancy could be attributed to the Gasteiger charges used within AutoDock4. We show that AutoDock4 is, surprisingly, not sensitive to the charges used, and the use of QC-derived atomic charges does not lead to any statistical improvements. We also briefly discuss the role of the explicit empirical hydrogen bond term along with the electrostatic term.

The role of aldehydes on sulfur based-new particle formation: a theoretical study

Aldehydes play a crucial role in the formation of atmospheric particles, attracting significant attention due to their environmental impact. However, the microscopic mechanisms underlying the formation of aldehyde-involved particles remain uncertain. In this study, through quantum chemical calculations and molecular dynamics (MD) simulations, we investigate the microscopic formation mechanisms of binary and ternary systems composed of three representative aldehydes, two sulfur-based acids, water, and two bases. Our research findings reveal that the most stable structures of acid-aldehyde clusters involve the connection of acids and aldehyde compounds through hydrogen bonds without involving proton transfer reactions, indicating relatively poor cluster stability. However, with the introduction of a third component, the stability of 18 clusters significantly increase. Among these, in ten systems, acids act as catalysts, facilitating reactions between aldehyde compounds and water or alkaline substances to generate glycols and amino alcohols. However, according to MD simulations conducted at 300 K, these acids readily dissociate from the resulting products. In the remaining eight systems, the most stable structural feature involves ion pairs formed by proton transfer reactions between acids and aldehyde compounds. These clusters exhibit remarkable thermodynamic stability. Furthermore, the acidity of the acid, the nature of nucleophilic agents, and the type of aldehyde all play significant roles in cluster stability and reactivity, and they have synergistic effects on the nucleation process. This study offers microscopic insights into the processes of new particle formation involving aldehydes, contributing to a deeper understanding of atmospheric chemistry at the molecular level.

Photochemical fate of quaternary ammonium compounds (QACs) and degradation pathways predication through computational analysis

Quaternary ammonium compounds (QACs) are commonly used in many products, such as disinfectants, detergents and personal care products. However, their widespread use has led to their ubiquitous presence in the environment, posing a potential risk to human and environmental health. Several methods, including direct and indirect photodegradation, have been explored to remove QACs such as benzylalkyldimethyl ammonium compounds (BACs) and alkyltrimethyl ammonium compounds (ATMACs) from the environment. Hence, in this research, a systematic review of the literature was conducted using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) method to understand the fate of these QACs during direct and indirect photodegradation in UV/H2O2, UV/PS, UV/PS/Cu2+, UV/chlorine, VUV/UV/chlorine, O3/UV and UV/O3/TiO2 systems which produce highly reactive radicals that rapidly react with the QACs, leading to their degradation. As a result of photodegradation, several transformation products (TPs) of QACs are formed, which can pose a greater risk to the environment and human health than the parent QACs. Only limited research in this area has been conducted with fewer QACs. Hence, quantum mechanical calculations such as density functional theory (DFT)-based computational calculations using Gaussian09 software package were used here to explain better the photo-resistant nature of a specific type of QACs, such as BACs C12-18 and ATMACs C12, C14, C18, and their transformation pathways, providing insights into active sites participating in the phototransformation. Recognizing that different advanced oxidation processes (AOPs) come with pros and cons in the elimination of QACs, this review also highlighted the importance of implementing each AOP concerning the formation of toxic transformation products and electrical energy per order (EEO), especially when QACs coexist with other emerging contaminants (ECs).

SQM2.20: Semiempirical quantum-mechanical scoring function yields DFT-quality protein-ligand binding affinity predictions in minutes

Accurate estimation of protein-ligand binding affinity is the cornerstone of computer-aided drug design. We present a universal physics-based scoring function, named SQM2.20, addressing key terms of binding free energy using semiempirical quantum-mechanical computational methods. SQM2.20 incorporates the latest methodological advances while remaining computationally efficient even for systems with thousands of atoms. To validate it rigorously, we have compiled and made available the PL-REX benchmark dataset consisting of high-resolution crystal structures and reliable experimental affinities for ten diverse protein targets. Comparative assessments demonstrate that SQM2.20 outperforms other scoring methods and reaches a level of accuracy similar to much more expensive DFT calculations. In the PL-REX dataset, it achieves excellent correlation with experimental data (average R2 = 0.69) and exhibits consistent performance across all targets. In contrast to DFT, SQM2.20 provides affinity predictions in minutes, making it suitable for practical applications in hit identification or lead optimization.

Docking and other computing tools in drug design against SARS-CoV-2

The use of computer simulation methods has become an indispensable component in identifying drugs against the SARS-CoV-2 coronavirus. There is a huge body of literature on application of molecular modelling to predict inhibitors against target proteins of SARS-CoV-2. To keep our review clear and readable, we limited ourselves primarily to works that use computational methods to find inhibitors and test the predicted compounds experimentally either in target protein assays or in cell culture with live SARS-CoV-2. Some works containing results of experimental discovery of corresponding inhibitors without using computer modelling are included as examples of a success. Also, some computational works without experimental confirmations are also included if they attract our attention either by simulation methods or by databases used. This review collects studies that use various molecular modelling methods: docking, molecular dynamics, quantum mechanics, machine learning, and others. Most of these studies are based on docking, and other methods are used mainly for post-processing to select the best compounds among those found through docking. Simulation methods are presented concisely, information is also provided on databases of organic compounds that can be useful for virtual screening, and the review itself is structured in accordance with coronavirus target proteins.

AROFRAG─A Systematic Approach for Fragmentation of Aromatic Molecules

We present a new systematic fragmentation scheme of polycyclic aromatic hydrocarbons (PAHs), including fullerenes and nanotubes, based on an idea to treat a sextet ring as a single unbreakable unit so that the basic unit of aromaticity remains preserved upon fragmentation. In the approach, denoted as AROFRAG (from aromatic fragmentation), a set of predefined elementary subsystems, such as naphthalene and biphenyl in the first model and larger PAHs in the second and third models, is generated with appropriate weights with the aim of reproducing the structure of the original molecule. The energies of the molecules are approximated as weighted sums of the energies of these subsystems. For symmetric cases, e.g., fullerenes, the point-group symmetry is preserved during the decomposition. The AROFRAG is used in conjunction with the molecule-in-molecule (MIM) technique to obtain an accurate description of the electronic energies. The new approach has been applied for selected graphene structures and fullerene doped with boron and nitrogen atoms, for which isomerization energies were calculated, as well as for several nanotubes and regular fullerene molecules. The combination of the third AROFRAG model and the MIM approach leads to the reproduction of electronic energies with a few milli-hartree accuracy at a fraction of the computational cost of the original method.

Improving protein-ligand docking results using the Semiempirical quantum mechanics: testing on the PDBbind 2016 core set

Molecular docking techniques are routinely employed for predicting ligand binding conformations and affinities in the in silico phase of the drug design and development process. In this study, a reliable semiempirical quantum mechanics (SQM) method, PM7, was employed for geometry optimization of top-ranked poses obtained from two widely used docking programs, AutoDock4 and AutoDock Vina. The PDBbind core set (version 2016), which contains high-quality crystal protein - ligand complexes with their corresponding experimental binding affinities, was used as an initial dataset in this research. It was shown that docking pose optimization improves the accuracy of pose predictions and is very useful for the refinement of docked complexes via removing clashes between ligands and proteins. It was also demonstrated that AutoDock Vina achieves a higher sampling power than AutoDock4 in generating accurate ligand poses (RMSD ≤ 2.0 Å), while AutoDock4 exhibits a better ranking power than AutoDock Vina. Finally, a new protocol based on a combination of the results obtained from the two docking programs was proposed for structure-based virtual screening studies, which benefits from the robust sampling abilities of AutoDock Vina and the reliable ranking performance of AutoDock4.Communicated by Ramaswamy H. Sarma.

Target-ligand binding affinity from single point enthalpy calculation and elemental composition

Reliable target-ligand binding thermodynamics data are essential for successful drug design and molecular engineering projects. Besides experimental methods, a number of theoretical approaches have been introduced for the generation of binding thermodynamics data. However, available approaches often neglect electronic effects or explicit water molecules influencing target-ligand interactions. To handle electronic effects within a reasonable time frame, we introduce a fast calculator QMH-L using a single target-ligand complex structure pre-optimized at the molecular mechanics level. QMH-L is composed of the semi-empirical quantum mechanics calculation of binding enthalpy with predicted explicit water molecules at the complex interface, and a simple descriptor based on the elemental composition of the ligand. QMH-L estimates the target-ligand binding free energy with a root mean square error (RMSE) of 0.94 kcal mol-1. The calculations also provide binding enthalpy values and they were compared with experimental binding thermodynamics data collected from the most reliable isothermal titration calorimetry studies of systems including various protein targets and challenging, large peptide ligands with a molecular weight of up to 2-3 thousand. The single point enthalpy calculations of QMH-L require modest computational resources and are based on short runs with open source and/or free software like Gromacs, Mopac, MobyWat, and Fragmenter. QMH-L can be applied for fast, automated scoring of drug candidates during a virtual screen, enthalpic engineering of new ligands or thermodynamic explanation of complex interactions.

Infrared Spectroscopy of Liquid Solutions as a Benchmarking Tool of Semiempirical QM Methods: The Case of GFN2-xTB

The accurate description of large molecular systems has triggered the development of new computational methods. Due to the computational cost of modeling large systems, the methods usually require a trade-off between accuracy and speed. Therefore, benchmarking to test the accuracy and precision of the method is an important step in their development. The typical gold standard for evaluating these methods is isolated molecules, because of the low computational cost. However, the advent of high-performance computing has made it possible to benchmark computational methods using observables from more complex systems such as liquid solutions. To this end, infrared spectroscopy provides a suitable set of observables (i.e., vibrational transitions) for liquid systems. Here, IR spectroscopy observables are used to benchmark the predictions of the newly developed GFN2-xTB semiempirical method. Three different IR probes (i.e., N-methylacetamide, benzonitrile, and semiheavy water) in solution are selected for this purpose. The work presented here shows that GFN2-xTB predicts central frequencies with errors of less than 10% in all probes. In addition, the method captures detailed properties of the molecular environment such as weak interactions. Finally, the GFN2-xTB correctly assesses the vibrational solvatochromism for N-methylacetamide and semiheavy water but does not have the accuracy needed to properly describe benzonitrile. Overall, the results indicate not only that GFN2-xTB can be used to predict the central frequencies and their dependence on the molecular environment with reasonable accuracy but also that IR spectroscopy data of liquid solutions provide a suitable set of observables for the benchmarking of computational methods.

MTA1, a Novel ATP Synthase Complex Modulator, Enhances Colon Cancer Liver Metastasis by Driving Mitochondrial Metabolism Reprogramming

Liver metastasis is the most fatal event of colon cancer patients. Warburg effect has been long challenged by the fact of upregulated oxidative phosphorylation (OXPHOS), while its mechanism remains unclear. Here, metastasis-associated antigen 1 (MTA1) is identified as a newly identified adenosine triphosphate (ATP) synthase modulator by interacting with ATP synthase F1 subunit alpha (ATP5A), facilitates colon cancer liver metastasis by driving mitochondrial bioenergetic metabolism reprogramming, enhancing OXPHOS; therefore, modulating ATP synthase activity and downstream mTOR pathways. High-throughput screening of an anticancer drug shows MTA1 knockout increases the sensitivity of colon cancer to mitochondrial bioenergetic metabolism-targeted drugs and mTOR inhibitors. Inhibiting ATP5A enhances the sensitivity of liver-metastasized colon cancer to sirolimus in an MTA1-dependent manner. The therapeutic effects are verified in xenograft models and clinical cases. This research identifies a new modulator of mitochondrial bioenergetic reprogramming in cancer metastasis and reveals a new mechanism on upregulating mitochondrial OXPHOS as the reversal of Warburg effect in cancer metastasis is orchestrated.

A semiempirical method optimized for modeling proteins

CONTEXT

In recent years, semiempirical methods such as PM6, PM6-D3H4, and PM7 have been increasingly used for modeling proteins, in particular enzymes. These methods were designed for more general use, and consequently were not optimized for studying proteins. Because of this, various specific errors have been found that could potentially cast doubt on the validity of these methods for modeling phenomena of biochemical interest such as enzyme catalytic mechanisms and protein-ligand interactions. To correct these and other errors, a new method specifically designed for use in organic and biochemical modeling has been developed.

METHODS

Two alterations were made to the procedures used in developing the earlier PMx methods. A minor change was made to the theoretical framework, which affected only the non-quantum theory interatomic interaction function, while the major change involved changing the training set for optimizing parameters, moving the focus to systems of biochemical significance. This involved both the selection of reference data and the weighting factors, i.e., the relative importance that the various data were given. As a result of this change of focus, the accuracy in prediction of heats of formation, hydrogen bonding, and geometric quantities relating to non-covalent interactions in proteins was improved significantly.

Electronic Structure Calculations of Static Hyper(Polarizabilities) of Substrate-Supported Group-IV and -V Elemental Monolayers

The substrate-induced effects on the polarizability (α) and first dipole hyperpolarizability (β) of group-IV (i.e., graphene, silicene, germanene, stanene) and group-V (i.e., phosphorene, arsenene, antimonene, and bismuthene) elemental monolayer nanoflakes are investigated. Density functional theory calculations show that these monolayers are bound with varying degrees of interaction strength with the Ag(111) substrate surface. Calculated dipole moment and β values are zero for the centrosymmetric configurations of the pristine elemental monolayers. On the other hand, substrate-induced changes in the electronic densities at the interface lead to substantially enhanced values of β, making these materials attractive for applications in the next-generation photonic technologies at the nanoscale.

How well do semiempirical QM methods describe the structure of proteins?

Semiempirical quantum-mechanical (QM) computational methods are an increasingly popular tool for the study of biomolecular systems. They were, however, developed and tested mostly on small model molecules. In this work, we explore one topic fundamental to these applications: the ability of the methods to describe the structure of proteins. In a set of 19 proteins for which a crystal structure with very high resolution is available, we analyze the properties of the protein geometries optimized using several semiempirical QM methods including PM6-D3H4, PM7, and GFN2-xTB. Some of the methods provide a very good description of the general structural features of the protein, yielding results better than or comparable to the AMBER ff03 force field. However, PM7 and PM6-D3H4 optimizations introduce artificial close contacts in the structure, which is partially remediated by reparameterization.

Photodegradation of a mixture of five pharmaceuticals commonly found in wastewater: Experimental and computational analysis

Photochemical transformation of pharmaceuticals plays an important role in their natural attenuation, especially in lagoon-based wastewater treatment plants and surface waters receiving substantial sunlight. In this study, the photodegradation of five important pharmaceuticals was studied in samples obtained from a wastewater treatment plant and surface water sources. Batch photodegradation studies for a mixture of pharmaceuticals (diclofenac, sulfamethoxazole, acetaminophen, carbamazepine and gemfibrozil) were carried out in a photochemical reactor. Multiple aliquots of samples removed from the reactor during the experiment were analyzed through high-performance liquid chromatography (HPLC) coupled to a photodiode array (PDA) detector. Intermediate products formed due to photodegradation were identified by ultra-high-performance liquid chromatography coupled with a time-of-flight mass spectrometry (UHPLC-MS/MS). Diclofenac and sulfamethoxazole were found to undergo direct photodegradation due to strong light absorption, whereas the indirect route of photosensitized degradation in the presence of dissolved organic matter (DOM) and model humic acid was significant for acetaminophen, carbamazepine, and gemfibrozil. The reactive radicals such as hydroxyl (OH^•), singlet oxygen (¹O₂) and excited states of DOM (*DOM) were predominantly responsible for the indirect photodegradation of acetaminophen, gemfibrozil and carbamazepine, respectively. Computational analysis revealed that chlorine and carbon atoms belonging to the benzene ring of diclofenac were more reactive to radical attack. Sulfamethoxazole photodegradation occurred through oxidation of the NH₂ group. Acetaminophen was more susceptible to electrophilic radical attack at the O-11, and N-7 positions and carbon atoms ortho to the phenolic oxygen and the amine group. The double bonds between C-7, C-8 and C-13 were the most reactive sites for carbamazepine that participated in the phototransformation pathway. Organic matter plays a critical role in the photodegradation of emerging contaminants. The coupling of DFT calculations with UHPLC-MS/MS analysis provided insights on key functional groups participating in the phototransformation pathway. Thus, both parent pharmaceuticals and the photodegradation intermediates should be considered during wastewater treatment.

Efficient Computation of the Interaction Energies of Very Large Non-covalently Bound Complexes

We present a new benchmark set consisting of 16 large non-covalently bound systems (LNCI16) ranging from 380 up to 1988 atoms and featuring diverse interaction motives. Gas-phase interaction energies are calculated with various composite DFT, semi-empirical quantum mechanical (SQM), and force field (FF) methods and are evaluated using accurate DFT reference values. Of the employed QM methods, PBEh-3c proves to be the most robust for large systems with a relative mean absolute deviation (relMAD) of 8.5% with respect to the reference interaction energies. r2SCAN-3c yields an even smaller relMAD, at least for the subset of complexes for which the calculation could be converged, but is less robust for systems with smaller HOMO–LUMO gaps. The inclusion of Fock-exchange is therefore important for the description of very large non-covalent interaction (NCI) complexes in the gas phase. GFN2-xTB was found to be the best performer of the SQM methods with an excellent result of only 11.1% deviation. From the assessed force fields, GFN-FF and GAFF achieve the best accuracy. Considering their low computational costs, both can be recommended for routine calculations of very large NCI complexes, with GFN-FF being clearly superior in terms of general applicability. Hence, GFN-FF may be routinely applied in supramolecular synthesis planning.

1 Introduction

2 The LNCI16 Benchmark Set

3 Computational Details

4 Generation of Reference Values

5 Results and Discussion

6 Conclusions

QSAR Studies on Nitrobenzene Derivatives using Hyperpolarizability and Conductor like Screening model as Molecular Descriptors

Quantitative structure-activity relationship (QSAR) models were useful in understanding how chemical structure relates to the toxicology of chemicals. In the present study, we report quantum molecular descriptors using conductor like screening model (COs) area, the linear polarizability, first and second order hyperpolarizability for modelling the toxicology of the nitro substituent on the benzene ring. All the molecular descriptors were performed using semi-empirical PM6 approaches. The QSAR model was developed using stepwise multiple linear regression. We found that the stable QSAR modelling of toxicology benzene derivatives used second order hyper-polarizability and COs area, which satisfied the statistical measures. The second order hyperpolarizability shows the best QSAR model. We also discovered that the nitrobenzene derivative’s substitutional functional group has a significant effect on the quantum molecular descriptors, which reflect the QSAR model.

ULYSSES: An Efficient and Easy to Use Semiempirical Library for C+

We introduce ULYSSES, a user-friendly and robust C++ library for semiempirical quantum chemical calculations. In its current version, ULYSSES is equipped with a large set of different semiempirical models, most of which are based on the Neglect of Diatomic Differential Overlap (NDDO) approximation. Empirical corrections for dispersion and hydrogen bonding are available for most methods, so that higher quality is achieved in the calculation of energies of nonbonded complexes. The library is furthermore equipped with geometry optimization, as well as modules for calculating molecular properties of general interest. Ideal gas thermodynamics is available and allows single structure as well as conformer (multistructure) averaged properties to be calculated. We offer the possibility to use several vibrational partition functions according to the nature of interactions being studied: for covalent systems, the traditional harmonic oscillator approximation is available; for nonbonded complexes, we systematically extended the partition function proposed by Grimme for all thermodynamic functions. The library is also capable of running Born-Oppenheimer molecular dynamics.

Recognition in the Domain of Molecular Chirality: From Noncovalent Interactions to Separation of Enantiomers

It is not a coincidence that both chirality and noncovalent interactions are ubiquitous in nature and synthetic molecular systems. Noncovalent interactivity between chiral molecules underlies enantioselective recognition as a fundamental phenomenon regulating life and human activities. Thus, noncovalent interactions represent the narrative thread of a fascinating story which goes across several disciplines of medical, chemical, physical, biological, and other natural sciences. This review has been conceived with the awareness that a modern attitude toward molecular chirality and its consequences needs to be founded on multidisciplinary approaches to disclose the molecular basis of essential enantioselective phenomena in the domain of chemical, physical, and life sciences. With the primary aim of discussing this topic in an integrated way, a comprehensive pool of rational and systematic multidisciplinary information is provided, which concerns the fundamentals of chirality, a description of noncovalent interactions, and their implications in enantioselective processes occurring in different contexts. A specific focus is devoted to enantioselection in chromatography and electromigration techniques because of their unique feature as "multistep" processes. A second motivation for writing this review is to make a clear statement about the state of the art, the tools we have at our disposal, and what is still missing to fully understand the mechanisms underlying enantioselective recognition.

Amino Acids Compete with Ammonia in Sulfuric Acid-Based Atmospheric Aerosol Prenucleation: The Case of Glycine and Serine

We present a computational investigation of the sulfuric acid, glycine, serine, ammonia, and water system to understand if this system can form prenucleation clusters, which are precursors to larger aerosols in the atmosphere. We have performed a comprehensive configurational search of all possible clusters in this system, starting with the four different monomers and zero to five waters. Accurate Gibbs free energies of formation have been calculated with the DLPNO-CCSD(T)/complete basis set (CBS) method on ωb97xd/6-31++G** geometries. For the dry dimers of sulfuric acid, the weakest base, serine, is found to form the most stable complex, which is a consequence of the strong di-ionic complex formed between the bisulfate ion and the protonated serine cation. For the dry dimers without sulfuric acid, the glycine-serine complex is more stable than the glycine-ammonia or serine-ammonia complexes, stemming from the detailed structure and not related to base strength. For the larger complexes, sulfuric acid deprotonates and the proton is shifted to glycine, serine, or ammonia. The two amino acids and ammonia are almost interchangeable and there is no easy way to predict which molecule will be protonated without the calculated results. Assuming reasonable starting concentrations and a closed system of sulfuric acid, glycine, serine, ammonia, and five waters, we predict the concentrations of all possible complexes at two temperatures spanning the troposphere. The most negative ΔG° values are a function of the detailed molecular interactions of these clusters. These details are more important than the base strength of ammonia, glycine, and serine.

How to Catch the Ball: Fullerene Binding to the Corannulene Pincer

The corannulene pincer (also known in the literature as the buckycatcher) is a fascinating system that may encapsulate, among other molecules, the C60 and C70 fullerenes. These complexes are held together by strong π-stacking interactions. Although these are quantum mechanical effects, their description by quantum chemical methods has proved very hard. We used three semi-empirical methods, PM6-D3H4X, PM6-D3H+ and GFN2-xTB, to model the interactions. Binding to fullerenes was extended to all open conformations of the buckycatcher, and with the proper choice of solvation model and partition functions, we obtained Gibbs free energies of binding that deviated by 1.0–1.5 kcal/mol from the experimental data. Adding three-body dispersion to PM6-D3H+ led to even better agreement. These results agree better with the experimental data than calculations using higher-level methods at a significantly lower fraction of the computational cost. Furthermore, the formation of adducts with C60 was studied using dynamical simulations, which helped to build a more complete picture of the behavior of the corannulene pincer with fullerenes. We also investigated the use of exchange-binding models to recover more information on this system in solution. Though the final Gibbs free energies in solution were worsened, gas-phase enthalpies and entropies better mirrored the experimental data.

Microscopic Insights Into the Formation of Methanesulfonic Acid–Methylamine–Ammonia Particles Under Acid-Rich Conditions

Understanding the microscopic mechanisms of new particle formation under acid-rich conditions is of significance in atmospheric science. Using quantum chemistry calculations, we investigated the microscopic formation mechanism of methanesulfonic acid (MSA)–methylamine (MA)–ammonia (NH3) clusters. We focused on the binary (MSA)2n-(MA)n and ternary (MSA)3n-(MA)n-(NH3)n, (n = 1–4) systems which contain more acid than base molecules. We found that the lowest-energy isomers in each system possess considerable thermodynamic and dynamic stabilities. In studied cluster structures, all bases are protonated, and they form stable ion pairs with MSA, which contribute to the charge transfer and the stability of clusters. MA and NH3 have a synergistic effect on NPF under acid-rich conditions, and the role of NH3 becomes more remarkable as cluster size increases. The excess of MSA molecules does not only enhance the stability of clusters, but provides potential sites for further growth.

Hydrogen-Bond Topology Is More Important Than Acid/Base Strength in Atmospheric Prenucleation Clusters

We explored the hypothesis that on the nanoscale level, acids and bases might exhibit different behavior than in bulk solution. Our study system consisted of sulfuric acid, formic acid, ammonia, and water. We calculated highly accurate Domain-based Local pair-Natural Orbital- Coupled-Cluster/Complete Basis Set (DLPNO-CCSD(T)/CBS) energies on DFT geometries and used the resulting Gibbs free energies for cluster formation to compute the overall equilibrium constants for every possible cluster. The equilibrium constants combined with the initial monomer concentrations were used to predict the formation of clusters at the top and the bottom of the troposphere. Our results show that formic acid is as effective as ammonia at forming clusters with sulfuric acid and water. The structure of formic acid is uniquely suited to form hydrogen bonds with sulfuric acid. Additionally, it can partner with water to form bridges from one side of sulfuric acid to the other, hence demonstrating that hydrogen bonding topology is more important than acid/base strength in these atmospheric prenucleation clusters.

The PM6-FGC Method: Improved Corrections for Amines and Amides

Recently, we reported a new approach to develop pairwise analytical corrections to improve the description of noncovalent interactions, by approximate methods of electronic structures, such as semiempirical quantum mechanical (SQM) methods. In particular, and as a proof of concept, we used the PM6 Hamiltonian and we named the method PM6-FGC, where the FGC acronym, corresponding to Functional Group Corrections, emphasizes the idea that the corrections work for specific functional groups rather than for individual atom pairs. The analytical corrections were derived from fits to B3LYP-D3/def2-TZVP (reference). PM6 interaction energy differences, evaluated for a reduced set of small bimolecular complexes, were chosen as representatives of saturated hydrocarbons, carboxylic, amine and, tentatively, amide functional groups. For the validation, the method was applied to several complexes of well-known databases, as well as to complexes of diglycine and dialanine, assuming the transferability of amine group corrections to amide groups. The PM6-FGC method showed great potential but revealed significant inaccuracies for the description of some interactions involving the –NH₂ group in amines and amides, caused by the inadequate selection of the model compound used to represent these functional groups (an NH₃ molecule). In this work, methylamine and acetamide are used as representatives of amine and amide groups, respectively. This new selection leads to significant improvements in the calculation of noncovalent interactions in the validation set.

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.

Deciphering deamidation and isomerization in therapeutic proteins: Effect of neighboring residue

Deamidation of asparagine (Asn) and isomerization of aspartic acid (Asp) residues are among the most commonly observed spontaneous post-translational modifications (PTMs) in proteins. Understanding and predicting a protein sequence's propensity for such PTMs can help expedite protein therapeutic discovery and development. In this study, we used proton-affinity calculations with semi-empirical quantum mechanics and microsecond long equilibrium molecular dynamics simulations to investigate mechanistic roles of structural conformation and chemical environment in dictating spontaneous degradation of Asn and Asp residues in 131 clinical-stage therapeutic antibodies. Backbone secondary structure, side-chain rotamer conformation and solvent accessibility were found to be key molecular indicators of Asp isomerization and Asn deamidation. Comparative analysis of backbone dihedral angles along with N-H proton affinity calculations provides a mechanistic explanation for the strong influence of the identity of the n + 1 residue on the rate of Asn/Asp degradation. With these findings, we propose a minimalistic physics-based classification model that can be leveraged to predict deamidation and isomerization propensity of proteins.

Semi-empirical and Linear-Scaling DFT Methods to Characterize duplex DNA and G-quadruplexes in Presence of Interacting Small Molecules

The computational study of DNA and its interaction with ligands is a highly relevant area of research, with significant consequences for developing new therapeutic strategies. However, the computational description of...

Monomers of Glycine and Serine Have a Limited Ability to Hydrate in the Atmosphere

The role of atmospheric aerosols on climate change is one of the biggest uncertainties in most global climate models. Organic aerosols have been identified as potential cloud condensation nuclei (CCN), and amino acids are organic molecules that could serve as CCN. Amino acids make up a significant portion of the total organic material in the atmosphere, and herein we present a systematic study of hydration for two of the most common atmospheric amino acids, glycine and serine. We compute DLPNO/CCSD(T)//M08-HX/MG3S thermodynamic properties and atmospheric concentrations of Gly(H₂O)_n and Ser(H₂O)_n, where n = 1-5. We predict that serine-water clusters have higher concentrations at n = 1 and 5, while glycine-water clusters have higher concentrations at n = 2-4. However, both glycine and serine are inferred to exist primarily in their nonhydrated monomer forms in the absence of other species such as sulfuric acid.

[Development of antiviral drugs based on inhibitors of the SARS-COV-2 main protease]

Docking and quantum-chemical methods have been used for screening of drug-like compounds from the own database of the Voronezh State University to find inhibitors the SARS-CoV-2 main protease, an important enzyme of the coronavirus responsible for the COVID-19 pandemic. Using the SOL program more than 42000 3D molecular structures were docked into the active site of the main protease, and more than 1000 ligands with most negative values of the SOL score were selected for further processing. For all these top ligands, the protein-ligand binding enthalpy has been calculated using the PM7 semiempirical quantum-chemical method with the COSMO implicit solvent model. 20 ligands with the most negative SOL scores and the most negative binding enthalpies have been selected for further experimental testing. The latter has been made by measurements of the inhibitory activity against the main protease and suppression of SARS-CoV-2 replication in a cell culture. The inhibitory activity \of the compounds was determined using a synthetic fluorescently labeled peptide substrate including the proteolysis site of the main protease. The antiviral activity was tested against SARS-CoV-2 virus in the Vero cell culture. Eight compounds showed inhibitory activity against the main protease of SARS-CoV-2 in the submicromolar and micromolar ranges of the IC50 values. Three compounds suppressed coronavirus replication in the cell culture at the micromolar range of EC50 values and had low cytotoxicity. The found chemically diverse inhibitors can be used for optimization in order to obtain a leader compound, the basis of new direct-acting antiviral drugs against the SARS-CoV-2 coronavirus.

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.

Docking Paradigm in Drug Design

Docking is in demand for the rational computer aided structure based drug design. A review of docking methods and programs is presented. Different types of docking programs are described. They include docking of non-covalent small ligands, protein-protein docking, supercomputer docking, quantum docking, the new generation of docking programs and the application of docking for covalent inhibitors discovery. Taking into account the threat of COVID-19, we present here a short review of docking applications to the discovery of inhibitors of SARS-CoV and SARS-CoV-2 target proteins, including our own result of the search for inhibitors of SARS-CoV-2 main protease using docking and quantum chemical post-processing. The conclusion is made that docking is extremely important in the fight against COVID-19 during the process of development of antivirus drugs having a direct action on SARS-CoV-2 target proteins.

Calculating Reliable Gibbs Free Energies for Formation of Gas-Phase Clusters that Are Critical for Atmospheric Chemistry: (H2SO4)3

The effects of atmospheric aerosols on our climate are one of the biggest uncertainties in global climate models. Calculating the pathway for the formation of pre-nucleation clusters that become aerosols is challenging, requiring a comprehensive analysis of configurational space and highly accurate Gibbs free energy calculations. We identified a large set of minimum energy configurations of (H₂SO₄)₃ using a sampling technique based on a genetic algorithm and a stepwise density functional theory (DFT) approach and computed the thermodynamics of formation of these configurations with more accurate wavefunction-based electronic energies computed on the DFT geometries. The DLPNO-CCSD(T) methods always return more positive energies compared to the DFT energies. Within the DLPNO-CCSD(T) methods, extrapolating to the complete basis set limit gives more positive free energies compared to explicitly correlated single-point energies. The CBS extrapolation was shown to be robust as both the 4-5 inverse polynomial and Riemann zeta function schemes were within chemical accuracy of one another.

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.

A new release of MOPAC incorporating the INDO/S semiempirical model with CI excited states

The semiempirical INDO/S Hamiltonian is incorporated into a new release of MOPAC2016. The MOPAC2016 software package has long been at the forefront of semiempirical quantum chemical methods (SEQMs) for small molecules, proteins, and solids and until this release has included only NDDO-type SEQMs. The new code enables the calculation of excited states using the INDO/S Hamiltonian combined with a configuration interaction (CI) approach using single excitations (CIS), single and double excitations (CISD), or multiple reference determinants (MRCI) where reference determinants are generated using a complete active space (CAS) approach. The capacity to perform excited-state calculations beyond the CIS level makes INDO/CI one of the few low-cost computational methods capable of accurately modeling states with substantial double-excitation character. Solvent corrections to the ground-state and excited-state energies can be computed using the COSMO implicit solvent model, incorporating state-specific corrections to the excited states based on the solvent refractive index. This code produces physically reasonable electronic structures, absorption spectra, and solvatochromic shifts at low computational costs for systems up to hundreds of atoms, and for both organic molecules and metal clusters.

Discovery of RTA ricin subunit inhibitors: a computational study using PM7 quantum chemical method and steered molecular dynamics

Ricin is a potent toxin derived from the castor bean plant and comprises two subunits, RTA and RTB. Because of its cytotoxicity, ricin has alarmed world authorities for its potential use as a chemical weapon. Ricin also affects castor bean agribusiness, given the risk of animal and human poisoning. Over the years, many groups attempted to propose small-molecules that bind to the RTA active site, the catalytic chain. Despite such efforts, there is still no effective countermeasure against ricin poisoning. The computational study carried out in the present work renews the discussion about small-molecules that may inhibit this toxin. Here, a structure-based virtual screening protocol capable of discerning active RTA inhibitors from inactive ones was performed to screen over 2 million compounds from the ZINC database to find novel scaffolds that strongly bind into the active site of the RTA. Besides, a novel score method based on ligand undocking force profiles and semi-empirical quantum chemical calculations provided insights into the rescore of docking poses. Summing up, the filtering steps pointed out seven main compounds, with the SCF00-451 as a promising candidate to inhibit the killing activity of such potent phytotoxin.

Enantioseparation, in vitro testing, and structural characterization of triple-binding reactivators of organophosphate-inhibited cholinesterases

The enantiomers of racemic 2-hydroxyimino-N-(azidophenylpropyl)acetamide-derived triple-binding oxime reactivators were separated, and tested for inhibition and reactivation of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) inhibited with tabun (GA), cyclosarin (GF), sarin (GB), and VX. Both enzymes showed the greatest affinity toward the methylimidazole derivative (III) of 2-hydroxyimino-N-(azidophenylpropyl)acetamide (I). The crystal structure was determined for the complex of oxime III within human BChE, confirming that all three binding groups interacted with active site residues. In the case of BChE inhibited by GF, oximes I (kr = 207 M-1 min-1) and III (kr = 213 M-1 min-1) showed better reactivation efficiency than the reference oxime 2-PAM. Finally, the key mechanistic steps in the reactivation of GF-inhibited BChE with oxime III were modeled using the PM7R6 method, stressing the importance of proton transfer from Nε of His438 to Oγ of Ser203 for achieving successful reactivation.

SQM/COSMO Scoring Function: Reliable Quantum-Mechanical Tool for Sampling and Ranking in Structure-Based Drug Design

Quantum mechanical (QM) methods have been gaining importance in structure-based drug design where a reliable description of protein-ligand interactions is of utmost significance. However, strategies i. e. QM/MM, fragmentation or semiempirical (SQM) methods had to be pursued to overcome the unfavorable scaling of QM methods. Various SQM-based approaches have significantly contributed to the accuracy of docking and improvement of lead compounds. Parametrizations of SQM and implicit solvent methods in our laboratory have been instrumental to obtain a reliable SQM-based scoring function. The experience gained in its application for activity ranking of ligands binding to tens of protein targets resulted in setting up a faster SQM/COSMO scoring approach, which outperforms standard scoring methods in native pose identification for two dozen protein targets with ten thousand poses. Recently, SQM/COSMO was effectively applied in a proof-of-concept study of enrichment in virtual screening. Due to its superior performance, feasibility and chemical generality, we propose the SQM/COSMO approach as an efficient tool in structure-based drug design.

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.

Supercomputer docking with a large number of degrees of freedom

Docking represents one of the most popular computational approaches in drug design. It has reached popularity owing to capability of identifying correct conformations of a ligand within an active site of the target-protein and of estimating the binding affinity of a ligand that is immensely helpful in prediction of compound activity. Despite many success stories, there are challenges, in particular, handling with a large number of degrees of freedom in solving the docking problem. Here, we show that SOL-P, the docking program based on the new Tensor Train algorithm, is capable to dock successfully oligopeptides having up to 25 torsions. To make the study comparative we have performed docking of the same oligopeptides with the SOL program which uses the same force field as that utilized by SOL-P and has common features of many docking programs: the genetic algorithm of the global optimization and the grid approximation. SOL has managed to dock only one oligopeptide. Moreover, we present the results of docking with SOL-P ligands into proteins with moveable atoms. Relying on visual observations we have determined the common protein atom groups displaced after docking which seem to be crucial for successful prediction of experimental conformations of ligands.

Enhancement of Atmospheric Nucleation by Highly Oxygenated Organic Molecules: A Density Functional Theory Study

New particle formation (NPF) by gas-particle conversion is the main source of atmospheric aerosols. Highly oxygenated organic molecules (HOMs) and sulfuric acid (SA) are important NPF participants. 2-Methylglyceric acid (MGA), a kind of HOMs, is a tracer of isoprene-derived secondary organic aerosols. The nucleation mechanisms of MGA with SA were studied using density functional theory and atmospheric cluster dynamics simulation in this study, along with that of MGA with methanesulfonic acid (MSA) as a comparison. Our theoretical works indicate that the (MGA)(SA) and (MGA)(MSA) clusters are the most stable ones in the (MGA) _i(SA) _j ( i = 1-2, j = 1-2) and (MGA) _i(MSA) _j ( i = 1-2, j = 1-2) clusters, respectively. Both the formation rates of (MGA)(SA) and (MGA)(MSA) clusters are quite large and could have significant contributions to NPF. The results imply that the homomolecular nucleation of MGA is unlikely to occur in the atmosphere, and MGA and SA can effectively contribute to heteromolecular nucleation mainly in the form of heterodimers. MSA exhibits properties similar to SA in its ability to form clusters with MGA but is slightly weaker than SA.

Explaining the interactions between metaldehyde and acidic surface groups of activated carbon under different pH conditions

Metaldehyde (MA) is an organic compound widely used in agriculture all around the world as molluscicide. There are growing concerns that relatively high levels of MA have been detected in surface water, which could be ascribed to the fact that it is transparent to common wastewater treatment processes. A theoretical study of the influence of activated carbon (AC) surface groups (SGs) on MA adsorption is done in order to help to understand the process and to evaluate the influence of the acid SGs over the adsorption in AC filters. Multiple Minima Hypersurface methodology was employed in order to study the interactions of the MA with acid SGs (hydroxyl and carboxyl) at acidic and neutral pH, and at different hydration conditions explicitly taking into account the solvent influence. Selected structures were re-optimized using Density Functional Theory and posteriorly refined to achieve a well-defined electron density to characterize the interactions by the Quantum Theory of Atoms in Molecules approach. The obtained results showed that the presence of SGs enhances the adsorption process. The deprotonated carboxyl and hydroxyl SGs of AC models show the strongest interactions, suggesting greater adsorption at neutral pH which is in concordance with experimental data. The main interactions are of a dispersive nature between the pesticide and the π-cloud of the AC and hydrogen bonds between the MA and the acid SGs suggesting that the adsorption process is driven by a physisorption mechanism. Water acts as an intermediary between the AC and MA and competing with it for the adsorption sites.

New factor Xa inhibitors based on 1,2,3,4-tetrahydroquinoline developed by molecular modelling

Factor Xa is a serine protease representing a crucial element in the coagulation process and an attractive target for anticoagulant therapy. At the present time there are several chemical classes of factor Xa inhibitors with proven activity. Furthermore, three factor Xa inhibitors have been approved for the medical use to date. However, therapy with these medications is accompanied by substantial adverse effects. In this background, the structure-based computational approach combining molecular docking and semiempirical quantum chemical calculations was applied for a search for new effective factor Xa inhibitors. We have undertaken a few virtual screening procedures to select potential candidates for synthesis and subsequent testing. The first screen of the focused library resulted in identifying 20 compounds among which 7 compounds showed the noticeable inhibition of factor Xa at maximal concentrations, allowed by solubility. The subsequent additional screens identified 20 additional candidates. Of these, 5 substances were shown to be capable of inhibiting factor Xa at 5 μM. The best two found 1,2,3,4-tetrahydroquinoline derivatives identified by means of modelling have demonstrated IC50 values in the micromolar range. One of them turned out to be selective factor Xa inhibitor over trypsin, factors IIa, IXa and XIa.

High-throughput quantum-mechanics/molecular-mechanics (ONIOM) macromolecular crystallographic refinement with PHENIX/DivCon: the impact of mixed Hamiltonian methods on ligand and protein structure

Conventional macromolecular crystallographic refinement relies on often dubious stereochemical restraints, the preparation of which often requires human validation for unusual species, and on rudimentary energy functionals that are devoid of nonbonding effects owing to electrostatics, polarization, charge transfer or even hydrogen bonding. While this approach has served the crystallographic community for decades, as structure-based drug design/discovery (SBDD) has grown in prominence it has become clear that these conventional methods are less rigorous than they need to be in order to produce properly predictive protein-ligand models, and that the human intervention that is required to successfully treat ligands and other unusual chemistries found in SBDD often precludes high-throughput, automated refinement. Recently, plugins to the Python-based Hierarchical ENvironment for Integrated Xtallography (PHENIX) crystallographic platform have been developed to augment conventional methods with the in situ use of quantum mechanics (QM) applied to ligand(s) along with the surrounding active site(s) at each step of refinement [Borbulevych et al. (2014), Acta Cryst D70, 1233-1247]. This method (Region-QM) significantly increases the accuracy of the X-ray refinement process, and this approach is now used, coupled with experimental density, to accurately determine protonation states, binding modes, ring-flip states, water positions and so on. In the present work, this approach is expanded to include a more rigorous treatment of the entire structure, including the ligand(s), the associated active site(s) and the entire protein, using a fully automated, mixed quantum-mechanics/molecular-mechanics (QM/MM) Hamiltonian recently implemented in the DivCon package. This approach was validated through the automatic treatment of a population of 80 protein-ligand structures chosen from the Astex Diverse Set. Across the entire population, this method results in an average 3.5-fold reduction in ligand strain and a 4.5-fold improvement in MolProbity clashscore, as well as improvements in Ramachandran and rotamer outlier analyses. Overall, these results demonstrate that the use of a structure-wide QM/MM Hamiltonian exhibits improvements in the local structural chemistry of the ligand similar to Region-QM refinement but with significant improvements in the overall structure beyond the active site.