Accurate Molecular Models Research Articles

Hydrogen Bonding in Amorphous Indomethacin.

Amorphous Indomethacin has enhanced bioavailability over its crystalline forms, yet amorphous forms can still possess a wide variety of structures. Here, Empirical Potential Structure Refinement (EPSR) has been used to provide accurate molecular models on the structure of five different amorphous Indomethacin samples, that are consistent with their high-energy X-ray diffraction patterns. It is found that the majority of molecules in amorphous Indomethacin are non-bonded or bonded to one neighboring molecule via a single hydrogen bond, in contrast to the doubly bonded dimers found in the crystalline state. The EPSR models further indicate a substantial variation in hydrogen bonding between different amorphous forms, leading to a diversity of chain structures not found in any known crystal structures. The majority of hydrogen bonds are associated with the carboxylic acid group, although a significant number of amide hydrogen bonding interactions are also found in the models. Evidence of some dipole-dipole interactions are also observed in the more structurally ordered models. The results are consistent with a distribution of Z-isomer intramolecular type conformations in the more disordered structures, that distort when stronger intermolecular hydrogen bonding occurs. The findings are supported by 1H and 2H NMR studies of the hydrogen bond dynamics in amorphous Indomethacin.

3DSGIMD: An accurate and interpretable molecular property prediction method using 3D spatial graph focusing network and structure-based feature fusion

A comprehensive representation of molecular structure is essential for establishing accurate and reliable molecular property prediction models. However, fully extracting and learning intrinsic molecular structure information, especially spatial structure features, remains a challenging task, leading that many molecular property prediction models still have no enough accuracy for the real application. In this study, we developed an innovative and interpretable deep learning method, termed 3DSGIMD, which predicted the molecular properties by integrating and learning the spatial structure and substructure information of molecules at multiple levels, and generated the focusing weights by aggregating spatial and adjacency information of molecules to improve understanding of prediction results. We evaluated the model on 10 public datasets and 14 cell-based phenotypic screening datasets. Extensive experimental results indicated that 3DSGIMD achieved superior or comparable predictive performance compared with some existing models, and the individually designed components contributed significantly to the advanced performance of the model. In addition, we also provided insight into the interpretability of our model via visualizing the focusing weights and perturbation analysis, and the results showed that 3DSGIMD can pinpoint crucial local structures and bits of molecular descriptors associated with the predicted properties. In summary, 3DSGIMD is a competitive molecular property prediction method that holds the potential to aid drug design and optimization.

Active learning graph neural networks for partial charge prediction of metal-organic frameworks via dropout Monte Carlo

Metal-organic frameworks (MOF) are an attractive class of porous materials due to their immense design space, allowing for application-tailored properties. Properties of interest, such as gas sorption, can be predicted in silico with molecular mechanics simulations. However, the accuracy is limited by the available empirical force field and partial charge estimation scheme. In this work, we train a graph neural network for partial charge prediction via active learning based on Dropout Monte Carlo. We show that active learning significantly reduces the required amount of labeled MOFs to reach a target accuracy. The obtained model generalizes well to different distributions of MOFs and Zeolites. In addition, the uncertainty predictions of Dropout Monte Carlo enable reliable estimation of the mean absolute error for unseen MOFs. This work paves the way towards accurate molecular modeling of MOFs via next-generation potentials with machine learning predicted partial charges, supporting in-silico material design.

Structure of the Bacterial Cell Envelope and Interactions with Antimicrobials: Insights from Molecular Dynamics Simulations.

Bacteria have evolved over 3 billion years, shaping our intrinsic and symbiotic coexistence with these single-celled organisms. With rising populations of drug-resistant strains, the search for novel antimicrobials is an ongoing area of research. Advances in high-performance computing platforms have led to a variety of molecular dynamics simulation strategies to study the interactions of antimicrobial molecules with different compartments of the bacterial cell envelope of both Gram-positive and Gram-negative species. In this review, we begin with a detailed description of the structural aspects of the bacterial cell envelope. Simulations concerned with the transport and associated free energy of small molecules and ions through the outer membrane, peptidoglycan, inner membrane and outer membrane porins are discussed. Since surfactants are widely used as antimicrobials, a section is devoted to the interactions of surfactants with the cell wall and inner membranes. The review ends with a discussion on antimicrobial peptides and the insights gained from the molecular simulations on the free energy of translocation. Challenges involved in developing accurate molecular models and coarse-grained strategies that provide a trade-off between atomic details with a gain in sampling time are highlighted. The need for efficient sampling strategies to obtain accurate free energies of translocation is also discussed. Molecular dynamics simulations have evolved as a powerful tool that can potentially be used to design and develop novel antimicrobials and strategies to effectively treat bacterial infections.

Block Chemistry for Accurate Modeling of Epoxy Resins.

Accurate molecular modeling of the physical and chemical behavior of highly cross-linked epoxy resins at the atomistic scale is important for the design of new property-optimized materials. However, a systematic approach to parametrizing and characterizing these systems in molecular dynamics is missing. We therefore present a unified scheme to derive atomic charges for amine-based epoxy resins, in agreement with the AMBER force field, based on defining reactive fragments─blocks─building the network. The approach is applicable to all stages of curing from pure liquid to gelation to fully cured glass. We utilize this approach to study DGEBA/DDS epoxy systems, incorporating dynamic topology changes into atomistic molecular dynamics simulations of the curing reaction with 127,000 atoms. We study size effects in our simulations and predict the gel point utilizing a rigorous percolation theory to recover accurately the experimental data. Furthermore, we observe excellent agreement between the estimated and the experimentally determined glass transition temperatures as a function of curing rate. Finally, we demonstrate the quality of our model by the prediction of the elastic modulus based on uniaxial tensile tests. The presented scheme paves the way for a broadly consistent approach for modeling and characterizing all amine-based epoxy resins.

Automatic Optimization of Lipid Models in the Martini Force Field Using SwarmCG.

After two decades of continued development of the Martini coarse-grained force field (CG FF), further refinment of the already rather accurate Martini lipid models has become a demanding task that could benefit from integrative data-driven methods. Automatic approaches are increasingly used in the development of accurate molecular models, but they typically make use of specifically designed interaction potentials that transfer poorly to molecular systems or conditions different than those used for model calibration. As a proof of concept, here, we employ SwarmCG, an automatic multiobjective optimization approach facilitating the development of lipid force fields, to refine specifically the bonded interaction parameters in building blocks of lipid models within the framework of the general Martini CG FF. As targets of the optimization procedure, we employ both experimental observables (top-down references: area per lipid and bilayer thickness) and all-atom molecular dynamics simulations (bottom-up reference), which respectively inform on the supra-molecular structure of the lipid bilayer systems and on their submolecular dynamics. In our training sets, we simulate at different temperatures in the liquid and gel phases up to 11 homogeneous lamellar bilayers composed of phosphatidylcholine lipids spanning various tail lengths and degrees of (un)saturation. We explore different CG representations of the molecules and evaluate improvements a posteriori using additional simulation temperatures and a portion of the phase diagram of a DOPC/DPPC mixture. Successfully optimizing up to ∼80 model parameters within still limited computational budgets, we show that this protocol allows the obtainment of improved transferable Martini lipid models. In particular, the results of this study demonstrate how a fine-tuning of the representation and parameters of the models may improve their accuracy and how automatic approaches, such as SwarmCG, may be very useful to this end.

Dual‐objective optimization for petroleum molecular reconstruction based on property and composition similarities

AbstractPetroleum molecular reconstruction method can be used to calculate molecular composition from limited analytical data, which is the basis of the molecular‐level process modeling of petroleum refining. However, due to the problem of multiple solutions, it is difficult to obtain accurate and stable molecular compositional models. Therefore, based on the traditional bulk property constraints, composition similarity was proposed and a new petroleum molecular reconstruction method was proposed. In this article, diesel is taken as the study object, and the method was applied to construct various diesel compositional models. The results showed that the models' bulk properties and molecular fraction distribution were consistent with the experimental data. Finally, the method was applied to construct diesel compositional models of an actual refinery. With the reference oil compositional model as the molecular reference data, the problem of accurate construction of compositional models in a long‐term production process without detailed characterization data was solved.

Development of a more accurate Geant4 quantum molecular dynamics model for hadron therapy

Objective. Although in heavy-ion therapy, the quantum molecular dynamics (QMD) model is one of the most fundamental physics models providing an accurate daughter-ion production yield in the final state, there are still non-negligible differences with the experimental results. The aim of this study is to improve fragment production in water phantoms by developing a more accurate QMD model in Geant4. Approach. A QMD model was developed by implementing modern Skyrme interaction parameter sets, as well as by incorporating with an ad hoc α-cluster model in the initial nuclear state. Two adjusting parameters were selected that can significantly affect the fragment productions in the QMD model: the radius to discriminate a cluster to which nucleons belong after the nucleus–nucleus reaction, denoted by R, and the squared standard deviation of the Gaussian packet, denoted by L. Squared Mahalanobis’s distance of fragment yields and angular distributions with 1, 2, and the higher atomic number for the produced fragments were employed as objective functions, and multi-objective optimization (MOO), which make it possible to compare quantitatively the simulated production yields with the reference experimental data, was performed. Main results. The MOO analysis showed that the QMD model with modern Skyrme parameters coupled with the proposed α-cluster model, denoted as SkM* α, can drastically improve light fragments yields in water. In addition, the proposed model reproduced the kinetic energy distribution of the fragments accurately. The optimized L in SkM* α was confirmed to be realistic by the charge radii analysis in the ground state formation. Significance. The proposed framework using MOO was demonstrated to be very useful in judging the superiority of the proposed nuclear model. The optimized QMD model is expected to improve the accuracy of heavy-ion therapy dosimetry.

Classical Exchange Polarization: An Anisotropic Variable Polarizability Model.

Polarizability, or the tendency of the electron distribution to distort under an electric field, often depends on the local chemical environment. For example, the polarizability of a chloride ion is larger in gas phase compared to a chloride ion solvated in water. This effect is due to the restriction the Pauli exclusion principle places on the allowed electron states. Because no two electrons can occupy the same state, when a highly polarizable atom comes in close contact with other atoms or molecules, the space of allowed states can dramatically decrease. This constraint suggests that an accurate molecular mechanics polarizability model should depend on the radial distance between neighboring atoms. This paper introduces a variable polarizability model within the framework of the HIPPO (Hydrogen-like Intermolecular Polarizable Potential) force field, by damping the polarizability as a function of the orbital overlap of two atoms. This effectively captures the quantum mechanical exchange polarization effects, without explicit utilization of antisymmetrized wave functions. We show that the variable polarizability model remarkably improves the two-body polarization energies and three-body energies of ion-ion and ion-water systems. Under this model, no manual tuning of atomic polarizabilities for monatomic ions is required; the gas-phase polarizability can be used because an appropriate damping function is able to correct the polarizability at short range.

A stochastic particle Fokker–Planck method with nonlinear production terms for a variable hard-sphere gas

The stochastic particle Fokker–Planck (FP) method has been gaining increasing attention in the field of rarefied gas dynamics due to its potential to reduce the computational costs of the direct simulation Monte Carlo method. The FP method approximates the discrete binary collisions of the Boltzmann equation as continuous drift–diffusion phenomena in velocity space. Consistency between the FP method and the Boltzmann equation is achieved by matching production terms. The Maxwell molecular model has been widely used in this process due to the possibility of obtaining closed-form solutions for these production terms. However, it is well known that the Maxwell molecular model has difficulty predicting strong shock waves since it cannot provide accurate relaxation rates for the moments. By contrast, the variable hard-sphere (VHS) molecular model is able to capture the transport properties of real gases better than the Maxwell molecular model. Nonetheless, there have so far been no reports associated with an accurate VHS molecular model for the stochastic particle FP method. In this paper, two different molecular models are developed to describe a monatomic gas interacting through a VHS potential. The proposed VHS molecular models are evaluated using Grad's 13- and 26-moment distribution functions; hence, they are named the G13 and G26 molecular models. The G13 and G26 molecular models include additional nonlinear moments compared with the conventional Maxwell molecular model. A one-dimensional shock wave and two-dimensional hypersonic cylinder flow are considered for validation. The results show that the proposed molecular models perform better than the Maxwell molecular model in predicting supersonic and hypersonic shock waves.

Simulation of the carbon dioxide hydrate-water interfacial energy

HypothesisCarbon dioxide hydrates are ice-like nonstoichiometric inclusion solid compounds with importance to global climate change, and gas transportation and storage. The thermodynamic and kinetic mechanisms that control carbon dioxide nucleation critically depend on hydrate-water interfacial free energy. Only two independent indirect experiments are available in the literature. Interfacial energies show large uncertainties due to the conditions at which experiments are performed. Under these circumstances, we hypothesize that accurate molecular models for water and carbon dioxide combined with computer simulation tools can offer an alternative but complementary way to estimate interfacial energies at coexistence conditions from a molecular perspective. CalculationsWe have evaluated the interfacial free energy of carbon dioxide hydrates at coexistence conditions (three-phase equilibrium or dissociation line) implementing advanced computational methodologies, including the novel Mold Integration methodology. Our calculations are based on the definition of the interfacial free energy, standard statistical thermodynamic techniques, and the use of the most reliable and used molecular models for water (TIP4P/Ice) and carbon dioxide (TraPPE) available in the literature. FindingsWe find that simulations provide an interfacial energy value, at coexistence conditions, consistent with the experiments from its thermodynamic definition. Our calculations are reliable since are based on the use of two molecular models that accurately predict: (1) The ice-water interfacial free energy; and (2) the dissociation line of carbon dioxide hydrates. Computer simulation predictions provide alternative but reliable estimates of the carbon dioxide interfacial energy. Our pioneering work demonstrates that is possible to predict interfacial energies of hydrates from a truly computational molecular perspective and opens a new door to the determination of free energies of hydrates.

Evaporation of liquid nanofilms: A minireview.

Evaporation of virus-loaded droplets and liquid nanofilms plays a significant role in the pandemic of COVID-19. The evaporation mechanism of liquid nanofilms has attracted much attention in recent decades. In this minireview, we first introduce the relationship between the evaporation process of liquid nanofilms and the pandemic of COVID-19. Then, we briefly provide the frontiers of liquid droplet/nanofilm evaporation on solid surfaces. In addition, we discuss the potential application of machine learning in liquid nanofilm evaporation studies, which is expected to be helpful to build up a more accurate molecular model and to investigate the evaporation mechanism of liquid nanofilms on solid surfaces.

Molecular Simulation of Resin and the Calculation of Molecular Bond Energy.

In this study, average structural characteristics of amber were researched and used as an example to establish the three-dimensional (3D) average structure of resin. Two coal samples containing solid amber were collected from Fushun and Hunchun in Northeast China, from which pure amber samples were separated and resin was extracted. Solid-state nuclear magnetic resonance (13C NMR) spectroscopy was used to obtain structural information of amber, and matrix-assisted laser desorption ionization time-of-flight mass spectrometry was performed on the resins to determine their molecular mass. The results of these studies revealed that the average structure of amber was dominated by cycloalkane, with a small amount of aromatic carbon, and there were almost no aliphatic chains in the structure. The molecular masses of the compounds in the resin were mainly in the range 99–750 Da, and the average molecular mass was ∼370 Da. To characterize the resin chemical structure, two 3D molecular models based on density functional theory were established taking amber as the example, and the relevant molecular bond energies were calculated. Based on these models, the interactions among the components in oil were studied, and the binding energies of the different molecules were calculated. In summary, in this study, amber was used as a medium to establish an accurate molecular model of resin and proved that compared to hydrocarbon compounds, resin molecules were more likely to interact with bitumen.

Searching for suitable lubricants for low global warming potential refrigerant R513A using molecular-based models: Solubility and performance in refrigeration cycles

• Accurate molecular thermodynamic model for R513A/lubricants in refrigeration cycles. • Rigorous energetic investigation of refrigeration cycles with thermodynamic postulates. • PEC5 optimal lubricant for R513A, in view of solubility, miscibility and viscosity. • Procedure allowed quantifying impact of the presence of lubricant on the COP. • Presence of PEC5 in the mixture up to 5% weight led to COP decrease of 3%. Following environmental regulations, refrigerant R134a (Global Warming Potential GWP = 1430), commonly used in air conditioning applications, is required to be replaced by refrigerants with lower GWP. Among them, R513A is investigated as an excellent alternative, with a GWP reduction of 56% compared to R134a. However, further studies are needed in order to assess its use for this application. In this work, the molecular-based polar soft-SAFT equation has been employed to predict the vapour-liquid equilibria (VLE) of R513A and selected lubricants, searching for the optimal lubricant and their performance on a vapour compression refrigeration cycle. First, the binary VLE of the commercial lubricants from the Pentaerythritol Esters (PECs) and Polyethylene Glycol Dimethyl Ethers (PEGDMEs) families with R1234yf and R134a were assessed versus available experimental data, by developing the appropriate molecular models and parameters. Then, the models were used to predict the properties of the ternary mixtures containing the two refrigerants and the lubricants. PEC5 was found to be the optimal lubricant for R513A, considering a balance between solubility, miscibility and viscosity. Molecular modelling combined with thermodynamic and energetic analysis allowed to calculate the performance of a vapour compression refrigeration cooling cycle, quantifying the impact of the presence of the lubricant on the COP. The presence of PEC5 in the mixture was not significant, staying below a COP decrease of 3% in all cases considered, while for TrEGDME, its presence reduced the COP up to 6.1%. The procedure used here allows tuning the performance of the refrigerant/lubricant pair, in a step forward on the search for low GWP refrigerants in air conditioning systems.

Developing and Using BioSIMAR, an Augmented Reality Program to Visualize and Learn about Chemical Structures in a Virtual Environment on Any Internet-Connected Device

Visualization can be a motivating way to teach students about molecules. Nowadays, the available experimental data and accurate computational results allow students to build realistic and accurate molecular models. These models include the representation of complex systems such as proteins, membranes, or nanotubes. However, the visualization of these three-dimensional (3D) structures can be challenging, complex, or even abstract for most people. To make the visualization easier and more motivating, BioSIM Augmented Reality (BioSIMAR) was developed as a free online software program to provide a new way to visualize and interact with 3D molecular models in a virtual environment. Using augmented reality (AR) and quick response code technologies, BioSIMAR provides tangible models of molecules to help illustrate chemical concepts. BioSIMAR runs on any electronic device, including mobile phones, tablets, laptops, or desktop computers with a camera and an internet connection, without requiring the installation of additional software. This software program helps people visualize and understand concepts about atoms and molecules, and their characteristics. BioSIMAR is freely available at https://ar.biosim.pt/.

A line-of-sight channel model for the 100\u2013450 gigahertz frequency band

This paper documents a simple parametric polynomial line-of-sight channel model for 100–450 GHz band. The band comprises two popular beyond fifth generation (B5G) frequency bands, namely, the D band (110–170 GHz) and the low-THz band (around 275–325 GHz). The main focus herein is to derive a simple, compact, and accurate molecular absorption loss model for the 100–450 GHz band. The derived model relies on simple absorption line shape functions that are fitted to the actual response given by complex but exact database approach. The model is also reducible for particular sub-bands within the full range of 100–450 GHz, further simplifying the absorption loss estimate. The proposed model is shown to be very accurate by benchmarking it against the exact response and the similar models given by International Telecommunication Union Radio Communication Sector. The loss is shown to be within ±2 dBs from the exact response for one kilometer link in highly humid environment. Therefore, its accuracy is even much better in the case of usually considered shorter range future B5G wireless systems.

New Techniques of Structure Elucidation for Sesquiterpenes.

The most significant new techniques that have been used in the twenty-first century for the structure elucidation of sesquiterpenes and some derivatives are reviewed in this chapter. A distinctive feature of these methodologies is the combination of accurate experimental measurements with theoretical data obtained by molecular modeling calculations that allow to visualize, understand, and quantify many structural characteristics. This has been the case for NMR spectroscopy, which has expanded its potential for solving complex structural problems by means of comparison with quantum mechanical molecular models. Ab initio and density functional theory calculations of chemical shifts, coupling constants, and residual chemical shift anisotropies have played important roles in the solution of many structures of sesquiterpenes. The assignments of their absolute configurations by evaluation of calculated and experimental chiroptical properties as electronic and vibrational circular dichroism are also reviewed. This chapter also includes the use of X-ray diffraction analysis with emphasis on calculations of the Flack and Hooft parameters, which are applicable to all molecules that crystallize in non-centrosymmetric space groups. The accurate molecular models of sesquiterpenes, validated by concordance with their experimental properties, are nowadays essential for the interpretation of the effects of these natural products on biological systems.

Rate Prediction for Homogeneous Nucleation of Methane Hydrate at Moderate Supersaturation Using Transition Interface Sampling.

The crystallization of methane hydrates via homogeneous nucleation under natural, moderate conditions is of both industrial and scientific relevance, yet still poorly understood. Predicting the nucleation rates at such conditions is notoriously difficult due to high nucleation barriers, and requires, besides an accurate molecular model, enhanced sampling. Here, we apply the transition interface sampling technique, which efficiently computes the exact rate of nucleation by generating ensembles of unbiased dynamical trajectories crossing predefined interfaces located between the stable states. Using an accurate atomistic force field and focusing on specific conditions of 280 K and 500 bar, we compute for nucleation directly into the sI crystal phase at a rate of ∼10–17 nuclei per nanosecond per simulation volume or ∼102 nuclei per second per cm3, in agreement with consensus estimates for nearby conditions. As this is most likely fortuitous, we discuss the causes of the large differences between our results and previous simulation studies. Our work shows that it is now possible to compute rates for methane hydrates at moderate supersaturation, without relying on any assumptions other than the force field.

Melting points of alkali chlorides evaluated for a polarizable and non-polarizable model.

Accurate molecular models of pure alkali halides are a prerequisite for developing transferable models of molten salts that can predict the properties of complex salt mixtures, such as those including dissolved actinide species and metal ions. Predicting the melting point of a substance represents a rigorous test of model quality. To this end, we compute the melting points of the alkali chlorides for a popular non-polarizable and polarizable model. Neither model yields more accurate predictions of the melting points across the entire family of alkali chlorides. Further calculations suggest that this may be because neither model simultaneously represents both the solid and liquid phases with sufficient accuracy across all four alkali chlorides. We find that the deviation from experiment in the model enthalpy of melting may be a good indicator of the deviation from experiment in the model melting temperature. Since the enthalpy of melting is easier to calculate in simulation than melting temperature, it may be a useful quantity to target when developing new force fields for molten salts.

Molecular Force Field Development for Aqueous Electrolytes: 2. Polarizable Models Incorporating Crystalline Chemical Potential and Their Accurate Simulations of Halite, Hydrohalite, Aqueous Solutions of NaCl, and Solubility.

The current state-of-the-art force fields (FFs) for Na+ and Cl- ions are not capable of simultaneously predicting the thermodynamic properties of the aqueous solution and the crystalline phase. This is primarily due to an oversimplification of the interaction models used but partially also due to the insufficient parametrization of the FFs. We have devised a straightforward and simple parametrization procedure for determining the ion-ion interaction parameters in complex molecular models of NaCl electrolytes which involves fitting the density, lattice energy, and chemical potential of crystalline NaCl at ambient conditions. Starting from the AH/BK3 and MAH/BK3 FFs, the parametrization approach is employed to develop a complex and accurate polarizable molecular model for the NaCl electrolyte by parametrizing the ion-ion interactions. The performance of the refined polarizable NaCl FF is assessed by evaluating the different thermodynamic and mechanical properties of the crystal, density of crystalline and molten NaCl, along with the melting temperature, properties of aqueous solutions, and the structure and stability of hydrohalite. The simulation results confirm the superiority of the refined FF in comparison with the existing state-of-the-art FFs to accurately predict a wide range of system properties in different NaCl phases, including NaCl aqueous solubility. The refined FF may find applications in the accurate simulations of NaCl electrolytes including inhomogeneous environment, phase equilibria and interfaces, and metastable states. Finally, the parametrization strategy is robust and general and can be used to devise molecular models for other electrolytes.