Atomic Charges Research Articles

Diffusion Properties of C9 Petroleum Resin in Porous γ-Al2O3: A Molecular Dynamics Study.

C9 petroleum resin hydrogenation is a classic macromolecular heterogeneous catalyzed reaction that can markedly improve the material's thermal stability and compatibility. The diffusion of these macromolecules within the catalyst pores significantly influences the mass transfer there and the overall hydrogenation process. However, due to experimental limitations, the diffusivity of reactants in the pores remains challenging to determine, which affects the accurate design and operation of the catalytic reaction process. A more realistic model of γ-Al2O3 was developed by incorporating atomic charge considerations, and a more precise interaction between the guest molecule and γ-Al2O3 was described using the INTERFACE force field. The model was characterized by calculating the accessible surface area, total pore volume, hydroxyl group density, wide-angle X-ray diffraction patterns, and benzene adsorption. The calculated results were validated and compared with the corresponding laboratory data. Molecular dynamics simulations were further employed to evaluate the diffusion behavior of C9 petroleum resin within the generated γ-Al2O3. The effects of temperature, the kinetic diameter of C9 petroleum resin, pore size, and pore window size of γ-Al2O3 on diffusion performance were examined. It was found that an increase in temperature can accelerate molecular diffusion, with larger molecules being more sensitive to temperature variations. A linear relationship between the diffusion coefficient and the kinetic diameter of C9 petroleum resin was observed for a given γ-Al2O3 pore structure. Additionally, the diffusion coefficient exhibited a parabolic dependence on pore size at a constant kinetic diameter, while the pore window size had a crucial influence on the diffusion of C9 petroleum resin. These results provide optimized operating conditions and valuable guidance for the preparation of feasible porous γ-Al2O3 catalysts for C9 petroleum resin hydrogenation.

Parallelized Tools for the Preparation and Curation of Large Libraries for Virtual Screening.

We introduce a software package, ParaLig, that provides solutions for several workflows occurring repeatedly in computational drug discovery: parameterization of small molecules with partial charges and free energies of solvation, generation of conformers, virtual chemical reactions, creating combinatorial libraries, and molecule editing tasks. Throughout, we emphasize the maintenance/creation of 3D coordinates and better interoperability. The latter is achieved by stable embedding of meta-information into output files and by improving the mutual compatibility of molecular representations (e.g., aromaticity perception). ParaLig wraps around core functionalities provided by a variety of existing software: the two popular cheminformatics packages OpenBabel and RDKit, and the general force field providers CGenFF and AmberTools. Our workflows emphasize scalable bulk processing of large libraries of molecules, and we provide an MPI-based wrapper to simplify deployment to high-performance computing resources. Along with salient descriptions of the methods, we benchmark performance both in terms of throughput and in terms of the quality of some of the results.

An efficient strategy for bdd electrode drive electro-catalysis triggering active species on lincomycin and antibiotic resistance genes removal: Electron transfer based on calculation modeling.

An efficient strategy for bdd electrode drive electro-catalysis triggering active species on lincomycin and antibiotic resistance genes removal: Electron transfer based on calculation modeling.

Shadow Molecular Dynamics with a Machine Learned Flexible Charge Potential.

We present an extended Lagrangian shadow molecular dynamics scheme with an interatomic Born-Oppenheimer potential determined by the relaxed atomic charges of a second-order charge equilibration model. To parametrize the charge equilibration model, we use machine learning with neural networks to determine the environment-dependent electronegativities and chemical hardness parameters for each atom, in addition to the charge-independent energy and force terms. The approximate shadow molecular dynamics potential in combination with the extended Lagrangian formulation improves the numerical stability and reduces the number of Coulomb potential calculations required to evaluate accurate conservative forces. We demonstrate efficient and accurate simulations with excellent long-term stability of the molecular dynamics trajectories. The significance of choosing fixed or environment-dependent electronegativities and chemical hardness parameters is evaluated. Finally, we compute the infrared spectrum of molecules via the dipole autocorrelation function and compare to experiments to highlight the accuracy of the shadow molecular dynamics scheme with a machine learned flexible charge potential.

ABCG2: A Milestone Charge Model for Accurate Solvation Free Energy Calculation.

In this report, we describe the development and validation of ABCG2, a new charge model with milestone free energy accuracy, while allowing instantaneous atomic charge assignment for arbitrary organic molecules. In combination with the second-generation general AMBER force field (GAFF2), ABCG2 led to a root-mean-square error (RMSE) of 0.99 kcal/mol on the hydration free energy calculation of all 642 solutes in the FreeSolv database, for the first time meeting the chemical accuracy threshold through physics-based molecular simulation against the golden-standard data set. Against the Minnesota Solvation Database, the solvation free energy calculation on 2068 pairs of a range of organic solutes in diverse solvents led to an RMSE of 0.89 kcal/mol. The 1913 data points of transfer free energies from the aqueous solution to organic solvents obtained an RMSE of 0.85 kcal/mol, corresponding to 0.63 log units for logP. The benchmark on densities of neat liquids for 1839 organic molecules and heat of vaporizations of 874 organic liquids achieved a comparable performance with the default restrained electrostatic potential (RESP) charge method of GAFF2. The fluctuations of assigned partial atomic charges over different input conformations from ABCG2 are demonstrated to be much smaller than those of RESP from statistics of 96 real drug molecules. The validation results demonstrated not only the accuracy but also the transferability and generality of the GAFF2/ABCG2 combination.

Efficacy of dispirotripiperazine PDSTP in a golden Syrian hamster model of SARS-CoV-2 infection

The increasing incidence of viral pandemics calls for new small-molecule therapeutics beyond traditional approaches and targets. Dispirotripiperazine, composed of two positively charged nitrogen atoms, represents an unusual scaffold in drug discovery campaigns, and molecules based on it are known to prevent virus infection by disrupting early host–pathogen interactions. In this study, the adhesion-blocking dispirotripiperazine core compound PDSTP was evaluated against SARS-CoV-2 in vitro and in vivo. We demonstrated that the molecule was acceptably active against two clinical isolates affecting the early stages of the SARS-CoV-2 cycle. In a hamster model of SARS-CoV-2 pneumonia, PDSTP treatment resulted in reduced viral loads in the lungs and turbinates and milder lung tissue lesions. Overall, these data support PDSTP as a preclinical candidate for the treatment of COVID-19.

Classification Method and Rational Adjustment of Asphalt Four Component Molecules Based on Quantum Chemical Calculation

ABSTRACTAsphalt is composed of four main components, with over 50 different molecules identified to characterize asphaltenes, resins, aromatics, and saturates. However, there has been a lack of sufficient quantitative demonstration to determine the rationality of these molecules representing their respective components. In this investigation, 64 types of molecules representing the four components of asphalt were collected to develop a more realistic molecular model. Quantum chemical calculations were performed to determine the Mayer bond order, HOMO–LUMO gap, molecular mass, sp2 hybrid carbon atom proportion, atomic charge, electrostatic potential, dipole moment, and molecular polarity index of the asphalt four‐component molecules. Based on the statistical analysis of the aforementioned indicators, the rationality of the classification was demonstrated, and appropriate adjustments were made. The results indicated that saturates could be classified by Mayer bond order; Asphaltenes could be classified by the HOMO–LUMO gap and molecular mass; Resins and aromatics could be classified by molecular polarity index, average Mayer bond order, and sp2 hybrid carbon atom proportion. This investigation provided a feasible method for classifying the four components of asphalt at the nanoscale and was expected to provide theoretical guidance for multi‐scale investigation on asphalt performance.

Advancing Multiscale Molecular Modeling with Machine Learning-Derived Electrostatics.

We introduce an innovative machine learning (ML)-based framework for multiscale molecular modeling in which the ML subsystem is treated as an electrostatic entity interacting with its molecular mechanics (MM) environment through classical electrostatics. The integration of ML accuracy with multiscale modeling is accomplished by leveraging the capabilities of the ANI neural networks to predict geometry-dependent atomic partial charges at the minimal basis iterative stockholder (MBIS) level, going beyond static mechanical embedding. This ML/MM approach can closely approximate state-of-the-art multiscale quantum-classical (QM/MM) methods while significantly lowering computational requirements, thereby facilitating more efficient and precise simulations in computational chemistry. The method requires no additional training beyond the initial model setup and is integrated into Amber, one of the most widely used software suites for molecular modeling, ensuring accessibility to the broader community. We validate its performance across a variety of challenging applications, including the solvation structure, vibrational spectra, torsion free energy profiles, and protein-ligand interactions, achieving excellent agreement with QM/MM benchmarks. This framework not only advances the frontiers of multiscale modeling but also showcases the potential of machine learning to achieve quantum-level accuracy with exceptional efficiency for complex chemical systems.

Synthesis, structure elucidation, SC-XRD/DFT, molecular modelling simulations and DNA binding studies of 3,5-diphenyl-4,5-dihydro-1H-pyrazole chalcones

Deoxyribonucleic acid (DNA) acts as the most important intracellular target for various drugs. Exploring the DNA binding interactions of small bioactive molecules offers a structural guideline for designing new drugs with higher clinical efficacy and enhanced selectivity. This study presents the facile synthesis of pyrazoline-derived compounds (4a)-(4f) by reacting substituted chalcones with hydrazine hydrate using formic acid. The structure elucidation of substituted pyrazoline compounds was carried out using 1H-NMR, FT-IR and elemental analyses. While the crystal structures of two compounds (4a) and (4b) have been resolved by single-crystal X-ray diffraction (SC-XRD) analysis. Hirshfeld surface analysis also endorsed their greater molecular stability. Computational calculations at DFT/B3LYP/6-311++G(d,p) were executed to compare the structural properties (bond angle and bond length) and explore reactivity descriptors, frontier molecular orbitals (FMO), Mulliken atomic charges (MAC), molecular electrostatic potential (MEP) and electronic properties. All the compounds were evaluated for DNA binding interactions by UV-Vis spectrophotometric analysis. The results revealed that compounds (4a)-(4f) bind to DNA via non-covalent binding mode having binding constant values ranging from 1.22 × 103 to 6.81 × 104 M−1. The negative values of Gibbs free energy also proved the interaction of studied compounds with DNA as a spontaneous process. The findings of molecular docking simulations depicted that these studied compounds showed significant binding interactions with DNA and these results were consistent with experimental findings. Compound (4b) was concluded as the most potent compound of the series with the highest binding constant (4.95 × 104) and strongest binding affinity (-8.48 kcal/mol).

Increasing the Accuracy and Robustness of the CHARMM General Force Field with an Expanded Training Set.

Small molecule empirical force fields (FFs), including the CHARMM General Force Field (CGenFF), are designed to have wide coverage of organic molecules and to rapidly assign parameters to molecules not explicitly included in the FF. Assignment of parameters to new molecules in CGenFF is based on a trained bond-angle-dihedral charge increment linear interpolation scheme for the partial atomic charges along with bonded parameters assigned based on analogy using a rules-based penalty score scheme associated with atom types and chemical connectivity. Accordingly, the accuracy of CGenFF is related to the extent of the training set of available parameters. In the present study that training set is extended by 1390 molecules selected to represent connectivities new to CGenFF training compounds. Quantum mechanical (QM) data for optimized geometries, bond, valence angle, and dihedral angle potential energy scans, interactions with water, molecular dipole moments, and electrostatic potentials were used as target data. The resultant bonded parameters and partial atomic charges were used to train a new version of the CGenFF program, v5.0, which was used to generate parameters for a validation set of molecules, including drug-like molecules approved by the FDA, which were then benchmarked against both experimental and QM data. CGenFF v5.0 shows overall improvements with respect to QM intramolecular geometries, vibrations, dihedral potential energy scans, dipole moments and interactions with water. Tests of pure solvent properties of 216 molecules show small improvements versus the previous release of CGenFF v2.5.1 reflecting the high quality of the Lennard-Jones parameters that were explicitly optimized during the initial optimization of both the CGenFF and the CHARMM36 force field. CGenFF v5.0 represents an improvement that is anticipated to more accurately model intramolecular geometries and strain energies as well as noncovalent interactions of drug-like and other organic molecules.

Recent Advances in the Construction and Application of Anion-Induced Cucurbit[n]uril-Based Supramolecular Assemblies via Outer-Surface Interactions.

Cucurbit[n]urils (abbreviated as Q[n]s or CB[n]s) are synthesized through the condensation of formaldehyde and glycoluril in a hydrochloric acid environment. These molecules exhibit three distinctive properties concerning their charge distribution: a neutral internal cavity, a positively charged outer surface, and a negatively charged portal carbonyl oxygen atoms. These unique characteristics facilitate the formation of supramolecular frameworks with a range of anions and metal polyanions. The primary focus of this review is on the creation of anion-induced Q[n]-based supramolecular self-assemblies and exploring their potential applications in metal ion sequestration and release, gas adsorption, as well as the development of fluorescence probes. Additionally, a concise review of current research on anion-induced Q[n]-based supramolecular compounds is provided, aiming to stimulate future advancements in the field of innovative supramolecular framework materials.

External electric field induced atomic charge migration and surface degradation of CsPbI3: A reactive molecular dynamics simulation based study

External electric field induced atomic charge migration and surface degradation of CsPbI3: A reactive molecular dynamics simulation based study

Structural Similarity, Activity, and Toxicity of Mycotoxins: Combining Insights from Unsupervised and Supervised Machine Learning Algorithms.

A large number of mycotoxins and related fungal metabolites have not been assessed in terms of their toxicological impacts. Current methodologies often prioritize specific target families, neglecting the complexity and presence of co-occurring compounds. This work addresses a fundamental question: Can we assess molecular similarity and predict the toxicity of mycotoxins in silico using a defined set of molecular descriptors? We propose a rapid nontarget screening approach for multiple classes of mycotoxins, integrating both unsupervised and supervised machine learning models, alongside molecular and physicochemical descriptors to enhance the understanding of structural similarity, activity, and toxicity. Clustering analyses identify natural clusters corresponding to the known mycotoxin families, indicating that mycotoxins belonging to the same cluster share similar molecular properties. However, topological descriptors play a significant role in distinguishing between acutely toxic and nonacutely toxic compounds. Random forest (RF) and neural networks (NN), combined with molecular descriptors, contribute to improved knowledge and predictive capability regarding mycotoxin toxicity profiles. RF allows the prediction of toxicity using data reflecting mainly structural features and performs well in the presence of descriptors reflecting biological activity. NN models prove to be more sensitive to biological activity descriptors than RF. The use of descriptors encompassing structural complexity and diversity, chirality and symmetry, connectivity, atomic charge, and polarizability, together with descriptors representing lipophilicity, absorption, and permeation of molecules, is crucial for predicting toxicity, facilitating broader toxicological evaluations.

State of Health Estimation of Lithium-Ion Batteries with Feature Interpretability Based on Partial Charge Curves

State of Health Estimation of Lithium-Ion Batteries with Feature Interpretability Based on Partial Charge Curves

Interpreting the Histidine-Containing Small Peptides on Tau Protein Tautomerism: A Theoretical Perspective.

Exploring the nature of histidine residue tautomerization via a systematic conformational study is essential for understanding the pathology and toxicity of several neurodegenerative diseases, as well as for their diagnosis and treatment. Herein, density functional theory (DFT) calculations were used to determine the Tau protein's histidine-containing dipeptide (Lys-His, His-Gln, and His-Val) and tripeptide (Lys-His-Gln and Lys-His-Val) isomeric conformations via intramolecular hydrogen bond interactions, with particular attention to the influence of N-H group isomeric forms on their properties. The calculated infrared (IR) spectroscopy of the N-H stretch region of each isomer and nuclear magnetic resonance (NMR) shielding of the imidazole ring carbon atoms (13C1, 13C2, and 13C3) were investigated. The results show that both the IR spectrum of the N-H group and the NMR shielding of 13C nuclei on the imidazole ring can be used to identify the histidine-containing dipeptide and tripeptide tautomeric isomers. Systematically analyzing the hydrogen bonding interactions, the atomic charge distribution, the potential energy distribution, and the HOMO-LUMO transitions of each isomer further verified the above conclusions. This study provides theoretical evidence for the conformation identification of the histidine-containing dipeptide and tripeptide isomers on Tau protein.

Predicting Ionic Conductivity of Imidazolium-Based Ionic Liquid Mixtures Using Quantum-Mechanically Derived Partial Charges in the Condensed Phase.

A considerable effort has been expended over the years to tune the properties of ionic liquids (ILs) by designing cations, anions, and pendant groups on the ions. A simple and effective approach to altering the properties of ILs is formulating IL-IL mixtures. However, the measurements and properties of such mixtures lag considerably behind those of pure ILs. From a molecular simulation point of view, binary IL mixtures have been investigated using charge distributions of pure ILs, which implicitly assumes that the ions of different polarizability do not influence the local electronic environment due to changing concentrations. To understand this effect, molecular dynamics (MD) simulations were conducted for a series of IL-IL mixtures containing the common cation 1-ethyl-3-methylimidazolium [C2mim] varying the composition of various combinations of anions (tetrafluoroborate [BF4] and dicyanamide [DCA], [BF4] and bis(trifluoromethanesulfonyl)imide [NTF2], [BF4] and trifluoromethanesulfonate [TFO], and [TFO] and [NTF2]). The effect of changing the electronic environment was evaluated by deriving partial charges using density functional theory (DFT) calculations in the condensed phase. It was observed that the overall charge on the cation and anion was a function of the cation-anion pairings for pure ILs. Moreover, the cation charge was found to vary linearly with anionic concentrations. Improved agreement of predicted density and ionic conductivity with experimental values was found for binary IL mixtures with this approach, in comparison to that when a fixed charge model is employed.

Sustainable Synthesis, DFT, Docking and In Vitro Evaluation of 6‐Mercaptopurine Syringic Acid Cocrystal: A Potent Drug for Breast Cancer Therapy

ABSTRACTThe 6‐mercaptopurine syringic acid (6MPSY) a new drug–drug cocrystal has been synthesized by the neat grinding method. The prepared cocrystal 6MPSY has been screened by the powder x‐ray diffraction procedure and the single phase was confirmed. Using density functional theory with B3LYP/6‐311++G (d, p) source set, the structural parameters were computed and associated with the structural parameters of the 6‐mercaptopurine molecule and syringic acid molecule reported earlier exhibit good agreement. Experimental FTIR and UV–vis spectra were documented to identify and analyze the group vibrational frequencies and electronic behavior of the cocrystal. The NBO study was accomplished and the inter‐ and intra‐molecular interactions were established. The inspection of the topological (MEP, ELF, LOL) and non‐covalent (RDG, IRI, DORI) contacts has exposed diverse groups of inter‐ and intra‐molecular links on the source of electron localization density and color gauge pointer, respectively. Mulliken and natural atomic charges have been calculated and depicted. Fukui studies gave the reactive site with high f+ and f− for C10, C17, C33, and H14 of the drug–drug cocrystal. The FMO analysis exhibits a low energy gap of 3.7612 eV that shows the efficiency of the drug–drug cocrystal in terms of chemical reactivity and stability. In addition, the thermodynamical parameters were calculated. The anti‐breast cancer activity of 6MPSY was investigated through in silico and in vitro analysis. The docking outcomes of 6MPSY range between −7.5 and −9.3 kcal/mol, and the calculated IC50 value in MDA‐MB‐231 breast cancer cells was 83.2 μg/mL.

Δ-EGNN Method Accelerates the Construction of Machine Learning Potential.

Recent advancements in molecular simulations highlight the substantial computational demands of generating high-precision quantum mechanical labels for training neural network potentials. These challenges emphasize the need for improvements in delta-machine learning techniques. The Equivariant Graph Neural Network (EGNN) framework, grounded in a message-passing mechanism that preserves structural equivariance, enables refined atomic representations through interaction-driven updates. We introduce the Δ-EGNN model, which achieves high prediction accuracy for both molecular and condensed-phase systems. For example, in periodic water box systems, a mean absolute error of 1.722 meV/atom for energy (global property) and 0.0027 e for partial charge (local property) were achieved with training on just 800 labels. Δ-EGNN is computationally efficient, achieving speedups of 1-2 orders of magnitude compared to conventional methods at the MP2 level. In contrast to models directly trained on total energies, such as NequIP, MACE, and Allegro, the Δ-EGNN model employs delta-machine learning to predict the difference between energies derived from low- and high-level electronic structure methods, providing a significant advantage in reducing computational costs while preserving high accuracy. In summary, Δ-EGNN opens a new avenue for exploring energy landscapes and constructing machine learning potentials with afforable computational overhead, facilitating routine quantum mechanical calculations for complex molecular systems.

Structural Regularities, Thermal Stability, and Nature of Chemical Bonding in the Series of Actinide Double Sulfates Cs[An(SO4)2(H2O)3]·H2O (An = U, Np, Pu, or Am).

Investigation of the properties of the trivalent light actinide compounds is hindered by their low stability under normal conditions. In this study, the An3+ double sulfates Cs[An(SO4)2(H2O)3]·H2O (An = U, Np, Pu, or Am) were synthesized and characterized by complementary methods. Their structure was solved using single-crystal X-ray diffraction (XRD), and peculiarities of the sulfate anion environment were addressed with vibrational spectroscopy. The oxidation states of the actinides were confirmed by using X-ray absorption near edge spectroscopy (XANES) and solid-state absorption spectroscopy. Changes in the local environment of Am ions caused by self-irradiation are observed after several months of storage. Decomposition of the compounds in air and in the inert atmosphere at temperatures up to 1000 °C and the final products were studied using thermal analysis and powder diffraction. Computational investigation employing approaches such as QTAIM, Löwdin bond order analysis, and atomic charge calculations was used to investigate trends in the nature of chemical bonds in these compounds. It is shown that the covalent interaction decreases from U to Am with a corresponding increase in ion charge.

Multi-center decomposition of molecular densities: A numerical perspective.

In this study, we analyze various Iterative Stockholder Analysis (ISA) methods for molecular density partitioning, focusing on the numerical performance of the recently proposed Linear approximation of Iterative Stockholder Analysis (LISA) model [Benda et al., J. Chem. Phys. 156, 164107 (2022)]. We first provide a systematic derivation of various iterative solvers to find the unique LISA solution. In a subsequent systematic numerical study, we evaluate their performance on 48 organic and inorganic, neutral and charged molecules and also compare LISA to two other well-known ISA variants: the Gaussian iterative stockholder analysis and Minimum Basis Iterative Stockholder analysis (MBIS). The study reveals that LISA-family methods can offer a numerically more efficient approach with better accuracy compared to the two comparative methods. Moreover, the well-known issue with the MBIS method, where atomic charges obtained for negatively charged molecules are anomalously negative, is not observed in LISA-family methods. Despite the fact that LISA occasionally exhibits elevated entropy as a consequence of the absence of more diffuse basis functions, this issue can be readily mitigated by incorporating additional or integrating supplementary basis functions within the LISA framework. This research provides the foundation for future studies on the efficiency and chemical accuracy of molecular density partitioning schemes.