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In this study, the saturated solubility of triethylamine hydrochloride (TEA·HCl) was determined using the static method in binary mixed solvents ((1-Butanol, dimethyl sulfoxide (DMSO), 1-Octanol) + dimethyl carbonate (DMC)) at temperatures ranging from 298.15 to 333.15 K and ambient pressure (p = 0.1 MPa). Quantum chemistry calculations were performed to analyze the dissolution process among different solvents. Results showed that the obtained solubility data correlated well with five equations. Evaluation of solubility data was carried out by mean Average Relative Deviation (ARD) and Root-Mean-Square Deviation (RMSD). The findings indicated that the modified Apelblat model demonstrated the strongest correlation among the five models. The ARD and 104 RMSD were 1.39% and 2.61, respectively. Subsequently, the Gibbs energy, enthalpy, and entropy of TEA·HCl dissolved in each mixed solvent can be determined by applying van't Hoff equations, revealing an endothermic and entropy-driven dissolution process. The experimental results indicated that the solubility of TEA·HCl in the selected binary solvents increased with the increasing temperature and decreased with the increasing molar fraction of DMC. The solubility sequence in various systems was explained in terms of the solvation free energy. The solubility values, model parameters, and thermodynamic properties of TEA·HCl in different mixed solvents can be obtained through experimentation, providing foundational support for its preparation, crystallization process, and further theoretical research. [ABSTRACT FROM AUTHOR]

Aging is a natural process that is also influenced by some factors like the food someone eats, lifestyle decisions, and impacts on general health. Despite the recognized role of nutrition in modulating the molecular and cellular mechanisms underlying aging, there is a lack of comprehensive exploration into potential interventions that can effectively mitigate these effects. In this study, we investigated the potential anti-aging properties of vitamin D by examining its interactions with key molecular targets involved in aging-related pathways. By using molecular docking and dynamics techniques, we evaluate the interactions and stability of vitamins D2 and D3 with key proteins involved in aging pathways, such as SIRT1, mTOR, AMPK, Klotho, AhR, and MAPK. Our results reveal promising binding affinities between vitamin D and SIRT1 forms, with energy values of −48.33 kJ/mol and −45.94 kJ/mol for vitamins D2 and D3, respectively, in aqueous environments. Moreover, molecular dynamics simulations revealed that the vitamin D3–SIRT1 complex exhibited greater stability compared with the vitamin D2–SIRT1 complex. The study calculated the solvation free energy to compare the solubility of vitamins D2 and D3 in water and various organic solvents. Despite their strong interactions with water, both vitamins exhibited low solubility, primarily due to the high energy cost associated with cavity formation in the aqueous environment. Compared with other solvents, water demonstrated particularly low solubility for both vitamins. This suggested that vitamins D2 and D3 preferred binding to aging receptors over dissolving in bulk aqueous environments, supporting their strong therapeutic interactions with these receptors. These findings shed light on the molecular mechanisms underlying vitamin D's potential anti-aging effects and lay the groundwork for developing nutraceuticals targeting aging and associated diseases. Understanding these mechanisms holds promise for future interventions aimed at promoting healthy aging and enhancing overall well-being. [ABSTRACT FROM AUTHOR]

A dense artificial neural network, ESE-ΔH-DNN, with two hidden layers for calculating both solvation free energies ΔG°solv and enthalpies ΔH°solv for neutral solutes in organic solvents is proposed. The input features are generalized-Born-type monatomic and pair electrostatic terms, the molecular volume, and atomic surface areas of the solute, as well as five easily available properties of the solvent. ESE-ΔH-DNN is quite accurate for ΔG°solv, with an RMSE (root mean square error) below 0.6 kcal/mol and an MAE (mean absolute error) well below 0.4 kcal/mol. It performs particularly well for alkane, aromatic, ester, and ketone solvents. ESE-ΔH-DNN also exhibits a fairly good accuracy for ΔH°solv prediction, with an RMSE below 1 kcal/mol and an MAE of about 0.6 kcal/mol.

A dense artificial neural network, ESE-ΔH-DNN, with two hidden layers for calculating both solvation free energies ΔG°solv and enthalpies ΔH°solv for neutral solutes in organic solvents is proposed. The input features are generalized-Born-type monatomic and pair electrostatic terms, the molecular volume, and atomic surface areas of the solute, as well as five easily available properties of the solvent. ESE-ΔH-DNN is quite accurate for ΔG°solv, with an RMSE (root mean square error) below 0.6 kcal/mol and an MAE (mean absolute error) well below 0.4 kcal/mol. It performs particularly well for alkane, aromatic, ester, and ketone solvents. ESE-ΔH-DNN also exhibits a fairly good accuracy for ΔH°solv prediction, with an RMSE below 1 kcal/mol and an MAE of about 0.6 kcal/mol. [ABSTRACT FROM AUTHOR]

The utilization of deep learning methodologies for addressing partial differential equations (PDEs) has garnered significant attention in recent years. This paper introduces an improved network structure tailored for the discontinuity-capturing, enabling the resolution of interface problem through a unified neural network framework. Employing the probability space filling argument, we show that our model can generate convergent sequences, where the convergence rate depends on the number of sampling points. Several numerical experiments with regular and irregular interfaces are conducted to elucidate the convergence characteristics, thereby validating the theoretical assertions. Furthermore, we apply our approach to effectively solve the size-modified Poisson-Boltzmann test model, utilizing it for predicting electrostatics and the solvation free energies for proteins immersed in ionic solvents, thus showcasing practical applications of our method. • Improved multi-scale fusion network uses only a single neural network. • Our network model is proposed to solve the elliptical interface problem. • Theoretically give a least convergence rate of our numerical method. • Numerical experiments agree with our theoretical convergence rate. • We calculate the solvation free energies of immersed biomolecules by our method. [ABSTRACT FROM AUTHOR]

Solvent plays an essential role in a variety of chemical, physical, and biological processes that occur in the solution phase. The reference interaction site model (RISM) and its three‐dimensional extension (3D‐RISM) serve as powerful computational tools for modeling solvation effects in chemical reactions, biological functions, and structure formations. We present the RISM integrated calculator (RISMiCal) program package, which is based on RISM and 3D‐RISM theories with fast GPU code. RISMiCal has been developed as an integrated RISM/3D‐RISM program that has interfaces with external programs such as Gaussian16, GAMESS, and Tinker. Fast 3D‐RISM programs for single‐ and multi‐GPU codes written in CUDA would enhance the availability of these hybrid methods because they require the performance of many computationally expensive 3D‐RISM calculations. We expect that our package can be widely applied for chemical and biological processes in solvent. The RISMiCal package is available at https://rismical-dev.github.io. [ABSTRACT FROM AUTHOR]

Aging is a natural process that is also influenced by some factors like the food someone eats, lifestyle decisions, and impacts on general health. Despite the recognized role of nutrition in modulating the molecular and cellular mechanisms underlying aging, there is a lack of comprehensive exploration into potential interventions that can effectively mitigate these effects. In this study, we investigated the potential anti-aging properties of vitamin D by examining its interactions with key molecular targets involved in aging-related pathways. By using molecular docking and dynamics techniques, we evaluate the interactions and stability of vitamins D2 and D3 with key proteins involved in aging pathways, such as SIRT1, mTOR, AMPK, Klotho, AhR, and MAPK. Our results reveal promising binding affinities between vitamin D and SIRT1 forms, with energy values of −48.33 kJ/mol and −45.94 kJ/mol for vitamins D2 and D3, respectively, in aqueous environments. Moreover, molecular dynamics simulations revealed that the vitamin D3–SIRT1 complex exhibited greater stability compared with the vitamin D2–SIRT1 complex. The study calculated the solvation free energy to compare the solubility of vitamins D2 and D3 in water and various organic solvents. Despite their strong interactions with water, both vitamins exhibited low solubility, primarily due to the high energy cost associated with cavity formation in the aqueous environment. Compared with other solvents, water demonstrated particularly low solubility for both vitamins. This suggested that vitamins D2 and D3 preferred binding to aging receptors over dissolving in bulk aqueous environments, supporting their strong therapeutic interactions with these receptors. These findings shed light on the molecular mechanisms underlying vitamin D’s potential anti-aging effects and lay the groundwork for developing nutraceuticals targeting aging and associated diseases. Understanding these mechanisms holds promise for future interventions aimed at promoting healthy aging and enhancing overall well-being.

Polymeric materials as drug carriers were applied to enhance the solubility of Silymarin. The structures (drug and drug-ethylene glycol derivatives compounds) were modeled in the gas phase by quantum mechanical calculations. Afterward, the optimized Silymarin-ethylene glycol derivatives were modeled in an aqueous environment, and solvation free energies and association free energies were calculated by Monte Carlo simulation and perturbation method. Diethylene glycol and diethylene glycol-dilactic acid could be associated with Silymarin in an aqueous solution. Application of ethylene glycol derivatives enhances the solvation free energy of Silymarin in aqueous media. [ABSTRACT FROM AUTHOR]

In 2012, Kim and Hirata derived two generalized Langevin equations (GLEs) for a biomolecule in water, one for the structural fluctuation of the biomolecule and the other for the density fluctuation of water, by projecting all the mechanical variables in phase space onto the two dynamic variables: the structural fluctuation defined by the displacement of atoms from their equilibrium positions, and the solvent density fluctuation. The equation has an expression similar to the classical Langevin equation (CLE) for a harmonic oscillator, possessing terms corresponding to the restoring force proportional to the structural fluctuation, as well as the frictional and random forces. However, there is a distinct difference between the two expressions that touches on the essential physics of the structural fluctuation, that is, the force constant, or Hessian, in the restoring force. In the CLE, this is given by the second derivative of the potential energy among atoms in a protein. So, the quadratic nature or the harmonicity is only valid at the minimum of the potential surface. On the contrary, the linearity of the restoring force in the GLE originates from the projection of the water's degrees of freedom onto the protein's degrees of freedom. Taking this into consideration, Kim and Hirata proposed an ansatz for the Hessian matrix. The ansatz is used to equate the Hessian matrix with the second derivative of the free-energy surface or the potential of the mean force of a protein in water, defined by the sum of the potential energy among atoms in a protein and the solvation free energy. Since the free energy can be calculated from the molecular mechanics and the RISM/3D-RISM theory, one can perform an analysis similar to the normal mode analysis (NMA) just by diagonalizing the Hessian matrix of the free energy. This method is referred to as the Generalized Langevin Mode Analysis (GLMA). This theory may be realized to explore a variety of biophysical processes, including protein folding, spectroscopy, and chemical reactions. The present article is devoted to reviewing the development of this theory, and to providing perspective in exploring life phenomena. [ABSTRACT FROM AUTHOR]

Performance of membranes for water purification is highly influenced by the interactions of solvated species with membrane surfaces, including surface adsorption of solutes upon fouling. Current efforts toward fouling-resistant membranes often pursue surface hydrophilization, frequently motivated by macroscopic measures of hydrophilicity, because hydrophobicity is thought to increase solute-surface affinity. While this heuristic has driven diverse membrane functionalization strategies, here we build on advances in the theory of hydrophobicity to critically examine the relevance of macroscopic characterizations of solute-surface affinity. Specifically, we use molecular simulations to quantify the affinities to model hydroxyl- and methyl-functionalized surfaces of small, chemically diverse, charge-neutral solutes represented in produced water. We show that surface affinities correlate poorly with two conventional measures of solute hydrophobicity, gas-phase water solubility and oil-water partitioning. Moreover, we find that all solutes show attraction to the hydrophobic surface and most to the hydrophilic one, in contrast to macroscopically based hydrophobicity heuristics. We explain these results by decomposing affinities into direct solute interaction energies (which dominate on hydroxyl surfaces) and water restructuring penalties (which dominate on methyl surfaces). Finally, we use an inverse design algorithm to show how heterogeneous surfaces, with multiple functional groups, can be patterned to manipulate solute affinity and selectivity. These findings, importantly based on a range of solute and surface chemistries, illustrate that conventional macroscopic hydrophobicity metrics can fail to predict solute-surface affinity, and that molecular-scale surface chemical patterning significantly influences affinity-suggesting design opportunities for water purification membranes and other engineered interfaces involving aqueous solute-surface interactions.

Based on the quantum chemical calculation software Gaussian 16, a computational chemistry design was carried out for the classical physical chemistry experiment "Determination of electromotive force". The solvation free energy of Cu2+ and Zn2+ was calculated by density functional theory, and then the standard molar Gibbs free energy change of the cell reaction was computed through the thermodynamic cycle. The standard electromotive force of the Cu-Zn glavanic battery was further derived. The experiment aims to deepen students' understanding of the basic physical chemistry concept, such as standard molar Gibbs free energy, electrode potential and Nernst equation, to give full play to students' subjective initiative, and to cultivate students' abilities of experiment designing, practice and scientific thinking. [ABSTRACT FROM AUTHOR]

Integral equation theory (IET) provides an effective solvation model for chemical and biological systems that balances computational efficiency and accuracy. We present a new software package, the expanded package for IET‐based solvation (EPISOL), that performs 3D‐reference interaction site model (3D‐RISM) calculations to obtain the solvation structure and free energies of solute molecules in different solvents. In EPISOL, we have implemented 22 different closures, multiple free energy functionals, and new variations of 3D‐RISM theory, including the recent hydrophobicity‐induced density inhomogeneity (HI) theory for hydrophobic solutes and ion‐dipole correction (IDC) theory for negatively charged solutes. To speed up the convergence and enhance the stability of the self‐consistent iterations, we have introduced several numerical schemes in EPISOL, including a newly developed dynamic mixing approach. We show that these schemes have significantly reduced the failure rate of 3D‐RISM calculations compared to AMBER‐RISM software. EPISOL consists of both a user‐friendly graphic interface and a kernel library that allows users to call its routines and adapt them to other programs. EPISOL is compatible with the force‐field and coordinate files from both AMBER and GROMACS simulation packages. Moreover, EPISOL is equipped with an internal memory control to efficiently manage the use of physical memory, making it suitable for performing calculations on large biomolecules. We demonstrate that EPISOL can efficiently and accurately calculate solvation density distributions around various solute molecules (including a protein chaperone consisting of 120,715 atoms) and obtain solvent free energy for a wide range of organic compounds. We expect that EPISOL can be widely applied as a solvation model for chemical and biological systems. EPISOL is available at https://github.com/EPISOLrelease/EPISOL. [ABSTRACT FROM AUTHOR]

Accurate estimation of solvation free energy (SFE) lays the foundation for accurate prediction of binding free energy. The Poisson‐Boltzmann (PB) or generalized Born (GB) combined with surface area (SA) continuum solvation method (PBSA and GBSA) have been widely used in SFE calculations because they can achieve good balance between accuracy and efficiency. However, the accuracy of these methods can be affected by several factors such as the charge models, polar and nonpolar SFE calculation methods and the atom radii used in the calculation. In this work, the performance of the ABCG2 (AM1‐BCC‐GAFF2) charge model as well as other two charge models, that is, RESP (Restrained Electrostatic Potential) and AM1‐BCC (Austin Model 1‐bond charge corrections), on the SFE prediction of 544 small molecules in water by PBSA/GBSA was evaluated. In order to improve the performance of the PBSA prediction based on the ABCG2 charge, we further explored the influence of atom radii on the prediction accuracy and yielded a set of atom radius parameters for more accurate SFE prediction using PBSA based on the ABCG2/GAFF2 by reproducing the thermodynamic integration (TI) calculation results. The PB radius parameters of carbon, oxygen, sulfur, phosphorus, chloride, bromide and iodine, were adjusted. New atom types, on, oi, hn1, hn2, hn3, were introduced to further improve the fitting performance. Then, we tuned the parameters in the nonpolar SFE model using the experimental SFE data and the PB calculation results. By adopting the new radius parameters and new nonpolar SFE model, the root mean square error (RMSE) of the SFE calculation for the 544 molecules decreased from 2.38 to 1.05 kcal/mol. Finally, the new radius parameters were applied in the prediction of protein‐ligand binding free energies using the MM‐PBSA method. For the eight systems tested, we could observe higher correlation between the experiment data and calculation results and smaller prediction errors for the absolute binding free energies, demonstrating that our new radius parameters can improve the free energy calculation using the MM‐PBSA method. [ABSTRACT FROM AUTHOR]

Understanding how protein–protein binding affinity is determined from molecular interactions at the interface is essential in developing protein therapeutics such as antibodies, but this has not yet been fully achieved. Among the major difficulties are the facts that it is generally difficult to decompose thermodynamic quantities into contributions from individual molecular interactions and that the solvent effect—dehydration penalty—must also be taken into consideration for every contact formation at the binding interface. Here, we present an atomic‐level thermodynamics analysis that overcomes these difficulties and illustrate its utility through application to SARS‐CoV‐2 neutralizing antibodies. Our analysis is based on the direct interaction energy computed from simulated antibody–protein complex structures and on the decomposition of solvation free energy change upon complex formation. We find that the formation of a single contact such as a hydrogen bond at the interface barely contributes to binding free energy due to the dehydration penalty. On the other hand, the simultaneous formation of multiple contacts between two interface residues favorably contributes to binding affinity. This is because the dehydration penalty is significantly alleviated: the total penalty for multiple contacts is smaller than a sum of what would be expected for individual dehydrations of those contacts. Our results thus provide a new perspective for designing protein therapeutics of improved binding affinity. [ABSTRACT FROM AUTHOR]

A new theoretical method, referred to as Generalized Langevin Mode Analysis (GLMA), is proposed to analyze the mode of structural fluctuations of a biomolecule in solution. The method combines the two theories in the statistical mechanics, or the Generalized Langevin theory and the RISM/3D‐RISM theory, to calculate the second derivative, or the Hessian matrix, of the free energy surface of a biomolecule in aqueous solution, which consists of the intramolecular interaction among atoms in the biomolecule and the solvation free energy. The method is applied to calculate the wave‐number spectrum of an alanine dipeptide in water for which the optical heterodyne‐detected Raman‐induced spectroscopy (RIKES) spectrum is available to compare with. The theoretical analysis reproduced the main features of the experimental spectrum with respect to the peak positions of the four bands around ~90 cm−1, ~240 cm−1, ~370 cm−1, and 400 cm−1, observed in the experimental spectrum, in spite that the physics involved in the two spectrum was not exactly the same: the experimental spectrum includes the contributions from the dipeptide and the water molecules interacting with the solute, while the theoretical one is just concerned with the solute molecule, influenced by solvation. Two major discrepancies between the theoretical and experimental spectra, one in the band intensity around ~100 cm−1, and the other in the peak positions around ~370 cm−1, are discussed in terms of the fluctuation mode of water molecules interacting with the dipeptide, which is not taken explicitly into account in the theoretical analysis. [ABSTRACT FROM AUTHOR]

Tools to predict vapor–liquid phase equilibria are indispensable for the conceptualization and design of separation processes. Modified separation of cohesive energy density (MOSCED) is a solubility-parameter-based method parameterized to make accurate predictions of the limiting activity coefficient. As a solubility-parameter-based method, MOSCED can not only make quantitative predictions, but can shed light on the underlying intermolecular interactions. In the present study, we demonstrated the ability of MOSCED to correlate the enthalpy of vaporization and vapor pressure at a specific temperature using multiple linear regression. With this addition, MOSCED is able to predict vapor–liquid phase equilibria in the absence of reference data. This was demonstrated for the prediction of the Henry's constant and solvation free energy of organic solutes in water, which was found to be superior to mod-UNIFAC. In addition to being able to make phase equilibrium predictions, the ability to correlate the enthalpy of vaporization and vapor pressure offers the opportunity to include additional properties in the regression of the MOSCED parameters. Given this success, we additionally attempted to correlate a wide range of physical properties using a similar expression. While, in some cases, the results were reasonable, they were inferior to the correlations of the enthalpy of vaporization and vapor pressure. Future efforts will be needed to improve the correlations. [ABSTRACT FROM AUTHOR]

We evaluate the predictive power of the PR + COSMOSAC Equation of State (EOS) for vapor pressure using a large dataset of 19,081 compounds. The PR + COSMOSAC EOS uses results of quantum mechanical solvation calculations to determine the energy and molecular volume parameters in the Peng‐Robinson EOS and thus does not require experimental critical temperature (Tc), pressure (Pc), and acentric factor (ω) as in the conventional approach. The prediction accuracy (average absolute relative deviation) is 141%, about seven times that of the PR EOS. A new approach is developed to improve the prediction accuracy from PR + COSMOSAC EOS by incorporating experimental normal boiling temperature. The prediction accuracy improves to 58%, about three times that of the PR EOS. The PR + COSMOSAC EOS is an effective method for vapor pressure prediction when no or just some experimental data is available. [ABSTRACT FROM AUTHOR]

• Multi-scale fusion neural network is proposed to solve the elliptical interface problem. • Multi-scale fusion neural network can better capture "sharp turns" on the interface. • Solutions by multi-scale fusion neural network can better preserve the C 0 continuity while keeping the flux jumps. • We apply multi-scale fusion neural network to calculate the electrostatic potential of immersed biomolecules. In this paper, we construct a novel multi-scale fusion network as a new deep learning structure to solve the elliptic interface problem. Compared with the results of the fully connected neural network and ResNet, the new multi-scale fusion network is shown to be able to better capture "sharp turns", leading to the improved accuracy. Furthermore, its numerical solutions can preserve the C 0 continuity of the solution while keeping the flux jumps passing through different interfaces, thus maintaining the physics of the differential equation. Then, as an application, the new method is applied to solve the three-dimensional Poisson-Boltzmann equations to calculate the electrostatic potential of immersed biomolecules. Numerical experiments demonstrate the effectiveness of our new method compared to the results obtained by the finite element method. [ABSTRACT FROM AUTHOR]

[Display omitted] The superiority of graphitic carbon nitride (g-C 3 N 4) nanosheet results from its large specific surface area, which can be achieved by exfoliation of the bulk layered structure. Liquid-phase exfoliation (LPE) is the best-known method for the synthesis of two-dimensional (2D) g-C 3 N 4 nanosheets. However, experimental investigations do not allow for a molecular-level understanding of the process. Molecular dynamics (MD) simulations are expected to provide microscopic insights and quantitative evaluation of the energy consumption during LPE, thus facilitating the search of effective solvents for the LPE of 2D materials. MD simulations are carried out to simulate the LPE process by performing potential of mean force calculations for the separation of two stacked g-C 3 N 4 nanosheets. Free energy of exfoliation is evaluated and compared among nine common solvents with distinct molecular structures. The most probable path for the exfoliation process is identified. The free energy of exfoliation is found to correlate directly with the solvent free energy of a single g-C 3 N 4 nanosheet. The solvation is enthalpy-driven and affected by the mobility of the solvent molecules around the nanosheet. Based on the MD results, several strategies are proposed to guide the selection of solvents for effective LPE. [ABSTRACT FROM AUTHOR]

Molecular design of redox-active materials with higher solubility and greater redox potential windows is instrumental in enhancing the performance of redox flow batteries Here we propose a computational procedure for a systematic evaluation of organic redox-active species by combining machine learning, quantum-mechanical, and classical density functional theory calculations. 1,517 small quinone molecules were generated from the building blocks of benzoquinone, naphthoquinone, and anthraquinone with different substituent groups. The physics-based methods were used to predict HOMO-LUMO gaps and solvation free energies that account for the redox potential differences and aqueous solubility, respectively. The high-throughput calculations were augmented with the quantitative structure-property relationship analyses and machine learning/graph network modeling to evaluate the materials’ overall behavior. The computational procedure was able to reproduce high-performance cathode electrolyte materials consistent with experimental observations and identify new electrolytes for RFBs by screening 100,000 di-substituted quinone molecules, the largest library of redox-active quinone molecules ever investigated. The efficient computational platform may facilitate a better understanding of the structure-function relationship of quinone molecules and advance the design and application of all-organic active materials for RFBs.

Predicting solvation free energies and describing the complex water behavior that plays an important role in essentially all biological processes is a major challenge from the computational standpoint. While an atomistic, explicit description of the solvent can turn out to be too expensive in large biomolecular systems, most implicit solvent methods fail to capture "dewetting" effects and heterogeneous hydration by relying on a pre-established (i.e., guessed) solvation interface. Here we focus on the Variational Implicit Solvent Method, an implicit solvent method that adds water "plasticity" back to the picture by formulating the solvation free energy as a functional of all possible solvation interfaces. We survey VISM's applications to the problem of molecular recognition and report some of the most recent efforts to tailor VISM for more challenging scenarios, with the ultimate goal of including thermal fluctuations into the framework. The advances reported herein pave the way to make VISM a uniquely successful approach to characterize complex solvation properties in the recognition and binding of large-scale biomolecular complexes.

In this study, molecular dynamics simulation was applied for calculating solvation free energy of 16 solute molecules in methanol solvent. The thermodynamic integration method was used because it was possible to calculate the difference in free energy in any thermodynamic path. After comparing results for solvation free energy in different force fields, COMPASS force field was selected since it had the lowest error compared to experimental result. Group-based summation method was used to compute electrostatic and van der Waals forces at 298.15 K and 1 atm. The results of solvation free energy were obtained from molecular dynamics simulation and were compared to the results from Solvation Model Density (SMD) and Universal Continuum Solvation Model (denoted as SM8), which were obtained from other research works. Average square-root-error for molecular dynamics simulation, SMD and SM8 models were 0.096091, 0.595798, and 0.70649. Furthermore, the coefficient of determination (R2) for molecular dynamics simulation was 0.9618, which shows higher accuracy of MD simulation for calculating solvation free energy comparing to two other models. [ABSTRACT FROM AUTHOR]

A comparison of computed 19F NMR chemical shifts and experiment provides evidence for large specific solvent effects for fluoride‐type anions interacting with the σ*(C−H) orbitals in organic solvents like MeCN or CH2Cl2. We show this for systems ranging from the fluoride ion and the bifluoride ion [FHF]− to polyhalogen anions [ClFx]−. Discrepancies between computed and experimental shifts when using continuum solvent models like COSMO or force‐field‐based descriptions like the 3D‐RISM‐SCF model show specific orbital interactions that require a quantum‐mechanical treatment of the solvent molecules. This is confirmed by orbital analyses of the shielding constants, while less negatively charged fluorine atoms (e. g. in [EF4]−) do not require such quantum‐mechanical treatments to achieve reasonable accuracy. The larger 19F solvent shift of fluoride in MeCN compared to water is due to the larger coordination number in the former. These observations are due to unusually strong charge‐assisted C−H⋅⋅⋅F− hydrogen bonds, which manifest beyond some threshold negative natural charge on fluorine of ca. < −0.6 e. The interactions are accompanied by sizable free energies of solvation, in the order F−≫[FHF]−>[ClF2]−>[ClF4]−. COSMO‐RS solvation free energies tend to moderately underestimate those from the micro‐solvated cluster treatment. Red‐shifted and intense vibrational C−H stretching bands, potentially accessible in bulk solution, are further spectroscopic finger prints. [ABSTRACT FROM AUTHOR]

The 3D-reference interaction site model (3D-RISM) molecular solvation theory in combination with the Kovalenko–Hirata (KH) closure is extended to seven heterocyclic liquids to understand their liquid states and to test the performance of the theory in solvation free energy (SFE) calculations of solutes in select solvents. The computed solvent site distribution profiles were compared with the all-atom molecular dynamics (MD) simulations, showing comparable performances. The computational results were compared against the structural parameters for liquids, whenever available, as well as against the experimental SFEs. The liquids are found to have local ordered structures held together via weak interactions in both the RISM and MD simulations. The 3D-RISM-KH computed SFEs are in good agreement with the benchmark values for the tetrahydrothiophene-S,S-dioxide, and showed comparatively larger deviations in the case of the SFEs in the tetrahydrofuran continuum. [ABSTRACT FROM AUTHOR]

Amorphous solid dispersions (ASDs) are a prevalent method for increasing the bioavailability and apparent solubility of poorly soluble drugs. Consequently, extensive research, encompassing both experimental and computational approaches, has been dedicated to developing methods for assessing the key factors influencing their stability, notably drug-polymer interactions. A common computational approach to rank the compatibility of a drug with a set of solvents or polymers is to compare thermodynamic observables, such as solvation free energies at infinite dilution. However, the impact of the molecular weight of the polymer excipient on these interactions remains underexplored. This study delves into this impact through atomistic simulations of Indomethacin in PVP(-VA) and HPMC, and through simulations using a coarse-grained model, emphasizing its critical importance. First, we demonstrate that the molecular weight of the polymer plays a pivotal role in determining the solvation free energy of the drug, at times exerting a more significant influence than the specific chemical identity of the polymer. Additionally, our simulations suggest that higher molecular weight polymers lead to lower solvation free energies and, thus, suggest better compatibility with the drug. Yet, the lower free energy of solvation of the drug in longer polymers does not translate into a higher solubility. This work highlights the subtle role polymer molecular weight plays when measuring thermodynamic observables in amorphous solid dispersions, a role which must be considered when optimizing pharmaceutical formulations.

Predicting molecular properties with the help of Neural Networks is a common way to substitute or enhance comprehensive quantum-chemical calculations. One of the problems facing researchers is the choice of vectorization approach to representing the solvent and the solute for the estimator model. In this work, 10 different approaches have been investigated for both organic solute and solvent including vectorizers that relied on macroscopic parameters, functional groups classification, molecular graphs, or atomic coordinates. A variation of the Bag of Bonds approach called JustBonds, trained on the MNSol database, showed the best overall performance resulting in RMSD <2 kcal/mol for the blind dataset that contains the solutes not presented in the training subset and <1 kcal/mol on records from Solv@TUM database, which is close to contemporary continuum models. We have also demonstrated that the most important bags usually contain heteroatom and play a key role in the solvation process. Furthermore, the small role of solvent vectorization was demonstrated and revealed that approaches based on functional groups or macroscopic solvent parameters are often enough to efficiently represent solvent media., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

The three-dimensional reference interaction site model of the molecular solvation theory with the Kovalenko–Hirata closure is used to calculate the free energy of solvation of organic solutes in liquid aliphatic ketones. The ketone solvent sites were modeled using a modified united-atom force field. The successful application of these solvation models in calculating ketone–water partition coefficients of a large number of solutes supports the validation and benchmarking reported here.

Abstract A comparison of computed 19F NMR chemical shifts and experiment provides evidence for large specific solvent effects for fluoride‐type anions interacting with the σ*(C−H) orbitals in organic solvents like MeCN or CH2Cl2. We show this for systems ranging from the fluoride ion and the bifluoride ion [FHF]− to polyhalogen anions [ClFx]−. Discrepancies between computed and experimental shifts when using continuum solvent models like COSMO or force‐field‐based descriptions like the 3D‐RISM‐SCF model show specific orbital interactions that require a quantum‐mechanical treatment of the solvent molecules. This is confirmed by orbital analyses of the shielding constants, while less negatively charged fluorine atoms (e. g., in [EF4]−) do not require such quantum‐mechanical treatments to achieve reasonable accuracy. The larger 19F solvent shift of fluoride in MeCN compared to water is due to the larger coordination number in the former. These observations are due to unusually strong charge‐assisted C−H⋅⋅⋅F− hydrogen bonds, which manifest beyond some threshold negative natural charge on fluorine of ca. [ClF2]−>[ClF4]−. COSMO‐RS solvation free energies tend to moderately underestimate those from the micro‐solvated cluster treatment. Red‐shifted and intense vibrational C−H stretching bands, potentially accessible in bulk solution, are further spectroscopic finger prints.

It has been increasingly common to investigate dynamic and thermodynamic properties of green solvents at atomistic scales with molecular simulation. In this work, we present a detailed evaluation of the widely used fixed-charge regime, i.e., the combination of scaled RESP charges and GAFF derivatives. The benchmark set contains three ionic liquids formed by cations 1-butyl-3-methylimidazolium and 1-hexyl-3-methylimidazolium and anions bis(trifluoromethylsulfonyl)imide and PF6. For the charge scaling issue, two physical properties including the mass density and the solvation free energies of external agents in ionic liquids are selected as the criteria. Large-scale fast-growth solvation free energy simulations are performed to obtain the solvation thermodynamics, and the results are further combined with hydration data to form a water-ionic-liquids transfer dataset, which considers the partition of solutes between water and ionic liquids. It is observed that the density-derived charge scaling factor is always smaller than that derived from solvation/partition thermodynamics, which supports the use of a slightly larger scaling factor to obtain a balanced description of solute-solvent and solvent-solvent interactions. For the bonded-terms assessment, we refit the transferable GAFF2 derivatives with generalized force-matching in a molecule- and component-specific manner. The results suggest that the bond-stretching and angle-bending terms in GAFF derivatives are often problematic, while the torsional potential shows satisfactory reproduction of ab initio results. Finally, combining the extensive computational perspectives accumulated in our series works, general guidelines for molecular modelling of ionic liquids with fixed-charge force fields are summarized.

Blind predictions of octanol/water partition coefficients and pKa at 298.15 K for 22 drug-like compounds were made for the SAMPL7 challenge. Octanol/water partition coefficients were predicted from solvation free energies computed using electronic structure calculations with the SM12, SM8 and SMD solvation models. Within these calculations we compared the use of gas- and solution-phase optimized geometries of the solute. Based on these calculations we found that in general the use of solution phase-optimized geometries increases the affinity of the solutes for water as compared to octanol, with the use of gas-phase optimized geometries resulting in the better agreement with experiment. The pKa is computed using the direct approach, scaled solvent-accessible surface model, and the inclusion of an explicit water molecule, where the latter two methods have previously been shown to offer improved predictions as compared to the direct approach. We find that the use of an explicit water molecule provides superior predictions, and that the predicted macroscopic pKa is sensitive to the employed microstates. [ABSTRACT FROM AUTHOR]

In molecular modelling of novel solvents such as ionic liquids, it is common to scale atomic charges to improve the experiment‐simulation agreement for some selected properties. As these liquids are designed to solvate solutes, whether the solvation thermodynamics could be correctly described is of utmost importance. Therefore, we present a comprehensive large‐scale calculation of solvation free energies via nonequilibrium fast‐switching simulations for a spectrum of molecules in ionic liquids, the atomic charges of which are scaled to maximize the prediction‐experiment correlation. Further, the density‐derived choice is compared with the solvation‐thermodynamics‐derived one. When the scaling factor is decreased, the density exhibits a monotonically decreasing behavior due to weaker inter‐molecular interactions produced by scaled atomic charges. However, solvation free energies do not show consistent monotonic behaviors, which are caused by competing electrostatic and vdW responses to the scaling‐parameter variation. More intriguingly, the solvation‐free‐energy‐derived scaling factor is generally slightly higher than the density‐derived one. We further calculate partition coefficients ortransfer free energies of solutes from water to ionic liquids to provide another thermodynamic perspective of charge scaling. Another central result is the detailed evaluation of the widely used force fields for bonded and vdW terms, i.e., the GAFF derivatives. [ABSTRACT FROM AUTHOR]

In this paper, a theoretical investigation is made regarding the possibility of using a water-soluble derivative of C60 as a drug delivery agent for treating Coronavirus disease 2019 (COVID-19). Molnupiravir is chosen as the transporting pharmaceutical compound since it has already proved to be very helpful in saving lives in case of hospitalization. According to the proposed formulation, a carboxyfullerene known as dendro[60]fullerene is externally connected with two molnupiravir molecules. Two properly formed nitrogen single bonds (N−N) are used as linkers between the dendro[60]fullerene and the two molnupiravir molecules to create the final form of the C60 derivate/molnupiravir conjugate. The energetics of the developed molecular system and its interaction with water and n-octanol are extensively studied via classical molecular dynamics (MD) using the COMPASS II force field. To study the interactions with water and n-octanol, an appropriate periodic amorphous unit cell is created that contains a single C60 derivative/molnupiravir system surrounded by numerous solvent molecules and simulated via MD in room conditions. In addition, the corresponding solvation-free energies of the investigated drug delivery system are computed and set in contrast with the corresponding properties of the water-soluble dendro[60]fullerene, to test its solubility capabilities. [ABSTRACT FROM AUTHOR]

[Display omitted] • Disparate responses of accuracy levels of bulk and solvation properties are observed along the temperature ladder. • The disparate responses are caused by the difference between interaction terms involved in different properties. • To maintain the same accuracy level, we need to increase the charge scaling factor for bulk density but do the opposite for solvation thermodynamics. • The general 0.8 charge scaling factor is a solution specific to the room temperature condition (298 K) and the target used for property-matching (i.e., mass density). • Our results highlight the necessity of performing condition- and property-specific parameter adjustments. Charge scaling as a treatment to effectively include the intermolecular charge transfer and polarization effects is widely applied in molecular modelling of ionic solvents. While the charge scaling factor of 0.8 is picked as a generally applicable solution usable in many computational works, in this work based on extremely precise molecular simulations of bulk solvents and nonequilibrium Hamiltonian switching, we highlight the fact that the general 0.8 selection of the charge scaling factor could be a solution specific to the room-temperature condition that most ionic-liquids studies perform. When the environmental condition varies, this 'general' recommendation could sometimes lead to huge deviations. The situation is further complicated by the disparate responses of different properties (bulk density and solvation free energy) in the charge scaling space and along the temperature ladder. The driving force triggering the disparate responses could be attributed to the difference between interaction components involved in different thermodynamic properties, i.e., solvent–solvent interactions for bulk density and the additional solute–solvent interaction in the solvation process. A caution hinted by these observations is the necessity of condition- and property-specific parameter adjustment in practical situations. [ABSTRACT FROM AUTHOR]

In this study, we investigated thermodynamic inconsistency in the potential of mean force (PMF) calculated using the Ornstein-Zernike (OZ), one-dimensional reference interaction site model (1D-RISM), and three-dimensional OZ (3D-OZ) theories. Two methods were used to obtain the PMF: converting the radial distribution function (RDF) into the PMF, and utilizing the solvation free energy (SFE) of the diatomic solute molecule with an added direct interaction between the two solute atoms. Both for the Lennard-Jones (LJ) and Coulomb systems, the hypernetted chain (HNC) closure showed the best performance among the closure relationships tested in this study. However, the performance of the HNC was not perfect and exhibited significant inconsistency in the PMF. The Kovalenko–Hirata and Kobryn–Gusarov–Kovalenko closures were also tested, with fair results when combined with the 1D-RISM equation but poor results when combined with the 3D-OZ equation. The Verlet-modified (VM) closure was found to be inapplicable to Coulomb systems. In contrast, the modified HNC (MHNC) closure showed relatively good results at high temperatures. Furthermore, we modified the Martynov–Sarkisov (MS) closure for Coulomb systems, resulting in a modified MS closure that yields relatively accurate PMF results. We also discuss the behavior of the SFE for Coulomb systems obtained from the 1D-RISM theory, a thorough analysis of both the RDF and the integrand in the Kirkwood charging formula. [ABSTRACT FROM AUTHOR]

[Display omitted] • Amide hydrogens form stronger H-bond with water molecules. • Solvation free energy of thymine have higher negative value for the OPLS model. • The rotational dynamics of thymine is slower compared to uracil. • Water–water H-bond lifetime is higher compared to uracil/thymine–water H-bonds. • Uracil and thymine do not have significant influence on the water dynamics. Molecular dynamics simulations of uracil and thymine are carried out at 308 K to investigate the hydrogen bonding structures and dynamics in an aqueous solution. Thymine shows higher hydrogen-bond interactions with water molecules compared to uracil for all the models compared here. The dipole rotation of thymine in water is significantly slower compared to uracil. Water–water hydrogen bond lifetime is higher compared to uracil/thymine–water hydrogen bonds. Karplus and Kollman models of thymine show higher solvation free energy (SFE). The SFE of uracil for Karplus and Kollman model is closer to the experimentally reported value (–16.06 kcal/mol) at 298 K. [ABSTRACT FROM AUTHOR]

We describe the theory of the so‐called common‐core/serial‐atom‐insertion (CC/SAI) approach to compute alchemical free energy differences and its practical implementation in a Python package called Transformato. CC/SAI is not tied to a specific biomolecular simulation program and does not rely on special purpose code for alchemical transformations. To calculate the alchemical free energy difference between several small molecules, the physical end‐states are mutated into a suitable common core. Since this only requires turning off interactions, the setup of intermediate states is straightforward to automate. Transformato currently supports CHARMM and OpenMM as back ends to carry out the necessary molecular dynamics simulations, as well as post‐processing calculations. We validate the method by computing a series of relative solvation free energy differences. [ABSTRACT FROM AUTHOR]

We describe here some tests we made in the SAMPL5 communal event of 'Semi-Explicit Assembly' (SEA), a recent method for computing solvation free energies. We combined the prospective tests of SAMPL5 with followup retrospective calculations, to improve two technical aspects of the field variant of SEA. First, SEA uses an approximate analytical surface around the solute on which a water potential is computed. We have improved and simplified the mathematical model of that surface. Second, some of the solutes in SAMPL5 were large enough to need a way to treat solvating waters interacting with 'buried atoms', i.e. interior atoms of the solute. We improved SEA with a buried-atom correction. We also compare SEA to Thermodynamic Integration molecular dynamics simulations, so that we can sort out force field errors.

Methods to predict the equilibrium solubility of nonelectrolyte solids are indispensable for early-stage process development, design, and feasibility studies. Conventional analytic methods typically require reference data to regress parameters, which may not be available or limited for novel systems. Molecular simulation is a promising alternative, but is computationally intensive. Here, we demonstrate the ability to use a small number of molecular simulation free energy calculations to generate reference data to regress model parameters for the analytical MOSCED (modified separation of cohesive energy density) model. The result is an efficient analytical method to predict the equilibrium solubility of nonelectrolyte solids. The method is demonstrated for the wastewater contaminants monuron, diuron, atrazine and atenolol. Predictions for monuron, diuron and atrazine are in reasonable agreement with MOSCED parameters regressed using experimental solubility data. Predictions for atenolol are inferior, suggesting a potential limitation in the adopted molecular models, or the solvents selected to generate the necessary reference data. [ABSTRACT FROM AUTHOR]

Four liquid aromatic nitrogen-containing compounds, viz. aniline, pyridine, 2-methylpyridine, and 2,6-dimethylpyridine, were analyzed with molecular dynamics simulations, three-dimensional reference interaction site model, and density functional theory calculations. These liquids are found to have multimeric ordered structures stabilized by π-stacking and C-H∙∙∙π interactions. The solutes solvation free energy computed with the reference interaction site model is in good agreement with the experimental results, and performs better than the conductor like polarizable continuum model used in electronic structure calculation.

Tools to predict vapor–liquid phase equilibria are indispensable for the conceptualization and design of separation processes. Modified separation of cohesive energy density (MOSCED) is a solubility-parameter-based method parameterized to make accurate predictions of the limiting activity coefficient. As a solubility-parameter-based method, MOSCED can not only make quantitative predictions, but can shed light on the underlying intermolecular interactions. In the present study, we demonstrated the ability of MOSCED to correlate the enthalpy of vaporization and vapor pressure at a specific temperature using multiple linear regression. With this addition, MOSCED is able to predict vapor–liquid phase equilibria in the absence of reference data. This was demonstrated for the prediction of the Henry’s constant and solvation free energy of organic solutes in water, which was found to be superior to mod-UNIFAC. In addition to being able to make phase equilibrium predictions, the ability to correlate the enthalpy of vaporization and vapor pressure offers the opportunity to include additional properties in the regression of the MOSCED parameters. Given this success, we additionally attempted to correlate a wide range of physical properties using a similar expression. While, in some cases, the results were reasonable, they were inferior to the correlations of the enthalpy of vaporization and vapor pressure. Future efforts will be needed to improve the correlations.

Local anesthetics are weak bases and alter their properties in accordance with their protonation state, which depends on the environmental pH. We studied the transport dynamics of several local anesthetics from the extracellular fluid across biological membranes to the axoplasm, in order to understand the effect of pH on their pharmacodynamic properties. By using experimental pKa values and n‐octanol/water partition coefficients, we calculated pH‐dependent distribution coefficients and therewith associated relative populations and free energy profiles between extracellular fluid, membrane, and axoplasm. We estimated the local anesthetic capacity of neural tissue, using a simple 2D model. All values were calculated under physiological conditions and under the effect of local acidosis. Under physiological conditions, local anesthetics were most prevalent in the membrane. Change of free energy for transfer across biological membrane and the difference in relative population between extracellular fluid and axoplasm was dependent only on the difference of pH values between both environments, and not on pKa values. Estimated storage capacities for long‐lasting local anesthetics are higher by a factor of 10 or 100 than for short‐lasting local anesthetics. The membrane does not represent a barrier for local anesthetic diffusion. Local anesthetics move from the compartment with higher pH to the compartment with lower pH, due to favorable solvation free energy of protonated species. The rate of transfer across biological membrane is diffusion‐controlled and is similar for all local anesthetics. Neural tissue has a higher storage capacity for highly lipophilic local anesthetics, which can explain their longer duration of action. [ABSTRACT FROM AUTHOR]

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The three-dimensional reference interaction site model of the molecular solvation theory with the Kovalenko–Hirata closure is used to calculate the free energy of solvation of organic solutes in liquid aliphatic ketones. The ketone solvent sites were modeled using a modified united-atom force field. The successful application of these solvation models in calculating ketone–water partition coefficients of a large number of solutes supports the validation and benchmarking reported here. [ABSTRACT FROM AUTHOR]

Blind predictions of octanol/water partition coefficients at 298.15 K for 22 drug-like compounds were made for the SAMPL7 challenge. The octanol/water partition coefficients were predicted using solvation free energies computed using molecular dynamics simulations, wherein we considered the use of both pure and water-saturated 1-octanol to model the octanol-rich phase. Water and 1-octanol were modeled using TIP4P and TrAPPE-UA, respectively, which have been shown to well reproduce the experimental mutual solubility, and the solutes were modeled using GAFF. After the close of the SAMPL7 challenge, we additionally made predictions using TIP4P/2005 water. We found that the predictions were sensitive to the choice of water force field. However, the effect of water in the octanol-rich phase was found to be even more significant and non-negligible. The effect of inclusion of water was additionally sensitive to the chemical structure of the solute. [ABSTRACT FROM AUTHOR]

We introduce a software package for the analysis of biomolecular solvation. The package collects computer codes that implement numerical methods for a variational implicit-solvent model (VISM). The input of the package includes the atomic data of biomolecules under consideration and the macroscopic parameters such as solute-solvent surface tension, bulk solvent density and ionic concentrations, and the dielectric coefficients. The output includes estimated solvation free energies and optimal macroscopic solute-solvent interfaces that are obtained by minimizing the VISM solvation free-energy functional among all possible solute-solvent interfaces enclosing the solute atoms. We review the VISM with various descriptions of electrostatics. We also review our numerical methods that consist mainly of the level-set method for relaxing the VISM free-energy functional and a compact coupling interface method for the dielectric Poisson-Boltzmann equation. Such numerical methods and algorithms constitute the central modules of the software package. We detail the structure of the package, format of input and output files, workflow of the codes, and the postprocessing of output data. Our demo application to a host-guest system illustrates how to use the package to perform solvation analysis for biomolecules, including ligand-receptor binding systems. The package is simple and flexible with respect to minimum adjustable parameters and a wide range of applications. Future extensions of the package use can include the efficient identification of ligand binding pockets on protein surfaces.