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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]

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]

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]

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]

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]

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]

[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]

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]

Within the scope of SAMPL7 challenge for predicting physical properties, the Integral Equation Formalism of the Miertus-Scrocco-Tomasi (IEFPCM/MST) continuum solvation model has been used for the blind prediction of n-octanol/water partition coefficients and acidity constants of a set of 22 and 20 sulfonamide-containing compounds, respectively. The log P and pKa were computed using the B3LPYP/6-31G(d) parametrized version of the IEFPCM/MST model. The performance of our method for partition coefficients yielded a root-mean square error of 1.03 (log P units), placing this method among the most accurate theoretical approaches in the comparison with both globally (rank 8th) and physical (rank 2nd) methods. On the other hand, the deviation between predicted and experimental pKa values was 1.32 log units, obtaining the second best-ranked submission. Though this highlights the reliability of the IEFPCM/MST model for predicting the partitioning and the acid dissociation constant of drug-like compounds compound, the results are discussed to identify potential weaknesses and improve the performance of the method. [ABSTRACT FROM AUTHOR]

Poisson-Boltzmann equation (PBE) is a classic implicit continuum model to predict the electrostatic potentials of a solvated biomolecule. In this paper, we present a finite volume element method specific to the elliptic interface problem with a non-homogeneous ux condition for solving PBE and provide a follow-up analysis. The new PBE solver is fulfilled through both Fortran and Python, afterwards the local Poisson test model coupled with an analytical solution is adopted to well validate the program. Lastly, an application of the new solver to the prediction of solvation free energies of the proteins is made. [ABSTRACT FROM AUTHOR]

We present a new strategy to estimate the temperature‐dependent vapor–liquid equilibria and solvation free energies of dilute neutral molecules based on only their estimated solvation energy and enthalpy at 298 K. These two pieces of information coupled with matching conditions between the functional forms developed by Japas and Levelt Sengers for near critical conditions and by Harvey for low and moderate temperature conditions allow the fitting of a piecewise function that predicts the temperature‐dependent solvation energy for dilute solutes up to the critical temperature of the solvent. If the Abraham and Mintz parameters for the solvent and solute are available or can be estimated from group contributions, this method requires no experimental data and can still provide accurate estimates with an error of about 1.6 kJ/mol. This strategy, which requires minimal computational resources, is shown to compare well with other methods of temperature‐dependent solvation free energy prediction. [ABSTRACT FROM AUTHOR]

All-atom molecular dynamics simulations with stratified alchemical free energy calculations were used to predict the octanol-water partition coefficient log P ow of eleven small molecules as part of the SAMPL6- log P blind prediction challenge using four different force field parametrizations: standard OPLS-AA with transferable charges, OPLS-AA with non-transferable CM1A charges, AMBER/GAFF, and CHARMM/CGenFF. Octanol parameters for OPLS-AA, GAFF and CHARMM were validated by comparing the density as a function of temperature, the chemical potential, and the hydration free energy to experimental values. The partition coefficients were calculated from the solvation free energy for the compounds in water and pure ("dry") octanol or "wet" octanol with 27 mol% water dissolved. Absolute solvation free energies were computed by thermodynamic integration (TI) and the multistate Bennett acceptance ratio with uncorrelated samples from data generated by an established protocol using 5-ns windowed alchemical free energy perturbation (FEP) calculations with the Gromacs molecular dynamics package. Equilibration of sets of FEP simulations was quantified by a new measure of convergence based on the analysis of forward and time-reversed trajectories. The accuracy of the log P ow predictions was assessed by descriptive statistical measures such as the root mean square error (RMSE) of the data set compared to the experimental values. Discarding the first 1 ns of each 5-ns window as an equilibration phase had a large effect on the GAFF data, where it improved the RMSE by up to 0.8 log units, while the effect for other data sets was smaller or marginally worsened the agreement. Overall, CGenFF gave the best prediction with RMSE 1.2 log units, although for only eight molecules because the current CGenFF workflow for Gromacs does not generate files for certain halogen-containing compounds. Over all eleven compounds, GAFF gave an RMSE of 1.5. The effect of using a mixed water/octanol solvent slightly decreased the accuracy for CGenFF and GAFF and slightly increased it for OPLS-AA. The GAFF and OPLS-AA results displayed a systematic error where molecules were too hydrophobic whereas CGenFF appeared to be more balanced, at least on this small data set. [ABSTRACT FROM AUTHOR]

Blind predictions of octanol/water partition coefficients at 298 K for 11 kinase inhibitor fragment like compounds were made for the SAMPL6 challenge. We used the conventional, "untrained", free energy based approach wherein the octanol/water partition coefficient was computed directly as the difference in solvation free energy in water and 1-octanol. We additionally proposed and used two different forms of a "trained" approach. Physically, the goal of the trained approach is to relate the partition coefficient computed using pure 1-octanol to that using water-saturated 1-octanol. In the first case, we assumed the partition coefficient using water-saturated 1-octanol and pure 1-octanol are linearly correlated. In the second approach, we assume the solvation free energy in water-saturated 1-octanol can be written as a linear combination of the solvation free energy in pure water and 1-octanol. In all cases here, the solvation free energies were computed using electronic structure calculations in the SM12, SM8, and SMD universal solvent models. In the context of the present study, our results in general do not support the additional effort of the trained approach. [ABSTRACT FROM AUTHOR]

The IEFPCM/MST continuum solvation model is used for the blind prediction of n-octanol/water partition of a set of 11 fragment-like small molecules within the SAMPL6 Part II Partition Coefficient Challenge. The partition coefficient of the neutral species (log P) was determined using an extended parametrization of the B3LYP/6-31G(d) version of the Miertus–Scrocco–Tomasi continuum solvation model in n-octanol. Comparison with the experimental data provided for partition coefficients yielded a root-mean square error (rmse) of 0.78 (log P units), which agrees with the accuracy reported for our method (rmse = 0.80) for nitrogen-containing heterocyclic compounds. Out of the 91 sets of log P values submitted by the participants, our submission is within those with an rmse < 1 and among the four best ranked physical methods. The largest errors involve three compounds: two with the largest positive deviations (SM13 and SM08), and one with the largest negative deviations (SM15). Here we report the potentiometric determination of the log P for SM13, leading to a value of 3.62 ± 0.02, which is in better agreement with most empirical predictions than the experimental value reported in SAMPL6. In addition, further inclusion of several conformations for SM08 significantly improved our results. Inclusion of these refinements led to an overall error of 0.51 (log P units), which supports the reliability of the IEFPCM/MST model for predicting the partitioning of neutral compounds. [ABSTRACT FROM AUTHOR]

We present a new size‐modified Poisson–Boltzmann ion channel (SMPBIC) model and use it to calculate the electrostatic potential, ionic concentrations, and electrostatic solvation free energy for a voltage‐dependent anion channel (VDAC) on a biological membrane in a solution mixture of multiple ionic species. In particular, the new SMPBIC model adopts a membrane surface charge density and a natural Neumann boundary condition to reflect the charge effect of the membrane on the electrostatics of VDAC. To avoid the singularity difficulties caused by the atomic charges of VDAC, the new SMPBIC model is split into three submodels such that the solution of one of the submodels is obtained analytically and contains all the singularity points of the SMPBIC model. The other two submodels are then solved numerically much more efficiently than the original SMPBIC model. As an application of this SMPBIC submodel partitioning scheme, we derive a new formula for computing the electrostatic solvation free energy. Numerical results for a human VDAC isoform 1 (hVDAC1) in three different salt solutions, each with up to five different ionic species, confirm the significant effects of membrane surface charges on both the electrostatics and ionic concentrations. The results also show that the new SMPBIC model can describe well the anion selectivity property of hVDAC1, and that the new electrostatic solvation free energy formula can significantly improve the accuracy of the currently used formula. © 2019 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Accurate solvation free energy ΔGsolv predictions require well parametrized force fields. In order to refit Lennard-Jones (LJ) parameters for improved ΔGsolv predictions for a variety of compound classes and chemical environments, a large number of ΔGsolv calculations is required. As the calculation of ΔGsolv is computational expensive, there is need for efficient but precise calculation methods. In this work, we focus on the computation of non-aqueous solvation free energies. We compare ΔGsolv results from highly precise reference simulations for transferring a solute from the vacuum into a condensed phase and results obtained from a thermodynamic cycle implementation. As test systems, we alter LJ parameters ε and σ of widely used GAFF atom types ca, cl, n1, oh and os in various solute/solvent combinations. We examine the degree of configurational space overlap and find an impact by hydrogen bonds and the solvent accessible surface area. We conclude that the application of a thermodynamic cycle for the parametrization of force fields targeting ΔGsolv is useful if the adaptation of LJ parameters is limited to atom types in the solute or if only ε is changed. [ABSTRACT FROM AUTHOR]

The IPolQ‐Mod charges, which are the average of two charge sets fitted in vacuum state and condensed phase, take account of polarization effect implicitly in the solvation free energy calculation. However, the performance of the IPolQ‐Mod charges sensitively depends on the QM levels used to generate the electrostatic potential from which the charges are fitted. In addition, the forces on atoms are not accurate theoretically in the molecular dynamics (MD) simulation as the solvent only feels the electrostatic potential of a half‐polarized density of the solute according to the derivation of the IPolQ‐Mod charges. To study these issues in detail, the IPolQ‐Mod charges are combined with the reference potential (RP) strategy to predict the solvation free energies in the present study. It is found that the thermodynamic perturbation (TP) corrections utilizing total energy difference and interaction energy difference are almost the same and free of bias. The solvation free energies estimated by the RP method match very well with those obtained by applying IPolQ‐Mod charges into MD simulation directly. By means of the RP strategy, the performances of the IPolQ‐Mod charges with the change of the strength of the exact HF exchange in several DFT functionals are determined effectively. Although the "optimal" strengths are found in B3LYP and LC‐ωPBE, the improvements over the default strength are not too much. In addition to the IPolQ‐Mod charges, other charge models like bond charge correction (BCC) charges could also be combined with the RP strategy to study the thermodynamic properties like solvation free energy. © 2019 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Development of novel in silico methods for questing novel PgP inhibitors is crucial for the reversal of multi-drug resistance in cancer therapy. Here, we report machine learning based binary classification schemes to identify the PgP inhibitors from non-inhibitors using molecular solvation theory with excellent accuracy and precision. The excess chemical potential and partial molar volume in various solvents are calculated for PgP± (PgP inhibitors and non-inhibitors) compounds with the statistical–mechanical based three-dimensional reference interaction site model with the Kovalenko–Hirata closure approximation (3D-RISM-KH molecular theory of solvation). The statistical importance analysis of descriptors identified the 3D-RISM-KH based descriptors as top molecular descriptors for classification. Among the constructed classification models, the support vector machine predicted the test set of Pgp± compounds with highest accuracy and precision of ~ 97% for test set. The validation of models confirms the robustness of state-of-the-art molecular solvation theory based descriptors in identification of the Pgp± compounds. [ABSTRACT FROM AUTHOR]

Misfolded Cu/Zn superoxide dismutase enzyme (SOD1) shows prion-like propagation in neuronal cells leading to neurotoxic aggregates that are implicated in amyotrophic lateral sclerosis (ALS). Tryptophan-32 (W32) in SOD1 is part of a potential site for templated conversion of wild type SOD1. This W32 binding site is located on a convex, solvent exposed surface of the SOD1 suggesting that hydration effects can play an important role in ligand recognition and binding. A recent X-ray crystal structure has revealed that 5-Fluorouridine (5-FUrd) binds at the W32 binding site and can act as a pharmacophore scaffold for the development of anti-ALS drugs. In this study, a new protocol is developed to account for structural (non-displaceable) water molecules in docking simulations and successfully applied to predict the correct docked conformation binding modes of 5-FUrd at the W32 binding site. The docked configuration is within 0.58 Å (RMSD) of the observed configuration. The docking protocol involved calculating a hydration structure around SOD1 using molecular theory of solvation (3D-RISM-KH, 3D-Reference Interaction Site Model-Kovalenko-Hirata) whereby, non-displaceable water molecules are identified for docking simulations. This protocol was also used to analyze the hydrated structure of the W32 binding site and to explain the role of solvation in ligand recognition and binding to SOD1. Structural water molecules mediate hydrogen bonds between 5-FUrd and the receptor, and create an environment favoring optimal placement of 5-FUrd in the W32 binding site. [ABSTRACT FROM AUTHOR]

The molecular solvation theory in the form of the Three-Dimensional Reference Interaction Site Model (3D-RISM) with Kovalenko–Hirata (KH) closure relation is benchmarked for use with dimethyl sulfoxide (DMSO) as solvent for (bio)-chemical simulation within the framework of integral equation formalism. Several force field parameters have been tested to correctly reproduce solvation free energy in DMSO, ion solvation in DMSO, and DMSO coordination prediction. Our findings establish a united atom (UA) type parameterization as the best model of DMSO for use in 3D-RISM-KH theory based calculations. [ABSTRACT FROM AUTHOR]

Mean‐field treatment of solvent provides an efficient technique to investigate chemical processes in solution in quantum mechanics/molecular mechanics (QM/MM) framework. In the algorithm, an iterative calculation is required to obtain the self‐consistency between QM and MM regions, which is a time‐consuming step. In the present study, we have proposed a noniterative approach by introducing a linear response approximation (LRA) into the solvation term in the one‐electron part of Fock matrix in a hybrid approach between molecular‐orbital calculations and a three‐dimensional (3D) integral equation theory for molecular liquids (multicenter molecular Ornstein–Zernike self‐consistent field [MC‐MOZ‐SCF]; Kido et al., J. Chem. Phys. 2015, 143, 014103). To save the computational time, we have also developed a fast method to generate electrostatic potential map near solute and the solvation term in Fock matrix, using Fourier transformation (FT) and real spherical harmonics expansion (RSHE). To numerically validate the LRA and FT‐RSHE method, we applied the present approach to water, carbonic acid, and their ionic species in aqueous solution. Molecular properties of the solutes were evaluated by the present approach with four different types of initial wave functions and compared with those by the original (MC‐MOZ‐SCF). We found that an initial wave function considering solvation effects is needed to appropriately reproduce the properties by MC‐MOZ‐SCF. Furthermore, a benchmark test for 32 solute molecules was performed to evaluate the accuracy of the present approach for solvation free energy (SFE) and measure the speedup ratio for MC‐MOZ‐SCF. The error of SFE for MC‐MOZ‐SCF does not correlate with the SFE but increases in proportion to the electronic reorganization energy. Similar to water and carbonic acid, an initial wave function with solvation effects is also important to make the error small. From the averaged speed up ratio, the present approach is 13.5 times faster than MC‐MOZ‐SCF. © 2019 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

• Simulation of hydroxycinnamic acids in cosolvent-modified supercritical CO 2 was performed. • Solvation free energies were calculated using the Bennett's acceptance ratio method. • Cosolvent-induced solubility enhancements have been estimated. • Cosolvent-induced effect decreases with increasing pressure. • Cosolvent-induced solubility enhancement correlates with number of solute's hydrogen bonds. Simulation of hydroxycinnamic acids (caffeic, p-coumaric, ferulic, synapic) in pure and modified by polar cosolvents (methanol, acetone) supercritical carbon dioxide has been carried out using classical molecular dynamics. Dependences of solvation free energies of the acids on the pressure and cosolvent mole fraction have been calculated using Bennett's acceptance ratio method. When a polar cosolvent is added, the solvation free energy decreases sharply, and with a subsequent increase in its concentration, it changes slightly. The cosolvent-induced effect decreases with increasing pressure. The structural parameters characterizing the selective solvation of the acids have been calculated. A correlation between the cosolvent-induced solubility enhancement and the average number of solute – cosolvent hydrogen bonds has been found. [ABSTRACT FROM AUTHOR]

In this paper, we calculate solvation free energies of several compounds in ionic liquids. These free energies are used to compute properties such as Henry's law constants and activity coefficients, generally required in the design of environmentally sustainable processes. It is known, for instance, that carbon dioxide from combustion is one of the main sources of anthropogenic greenhouse gases. Recently, the propensity of the ionic liquid 1-ethyl-3-methylimidazolium tetracyanoborate ([ emim ] [ B (CN) 4 ]) for the physisorption of CO 2 has been reported, which makes it a potential solvent for carbon capture. In the present work, molecular dynamics simulations of the solvation of CO 2 in ionic liquids [ emim ] [ B (CN) 4 ] and 1-ethyl-3-methylimidazolium bis (trifluoromethylsulfonyl) imide ([ emim ] [ NTf 2 ]) are carried out both at infinite dilution and at high concentrations. A systematic study is performed by comparing several force fields and assessing the efficacy of simplifications in the simulations by using rigid-body dynamics and pairwise electrostatics. Our results confirm recent experimental observations that, for a given volume of solvent, lower pressure is required to absorb a certain amount of CO 2 in [ emim ] [ B (CN) 4 ] than in other ionic liquids. [ABSTRACT FROM AUTHOR]

The state-of-the-art molecular solvation theory is used to predict skin permeability of a large set of compounds with available experimental skin permeability coefficient (logKP). Encouraging results are obtained pointing to applicability of a novel quantitative structure activity model that uses statistical physics based 3D-RISM-KH theory for solvation free energy calculations as a primary descriptor for the prediction of logKP with relative mean square error of 0.77 units. [ABSTRACT FROM AUTHOR]

Abstract Solvation free energies can be essential in the process of evaluating and developing force fields. In addition, they can be used to obtain a diversity of other thermodynamic properties such as Henry's law constants and partition coefficients, for instance. In this paper, we study the solvation of asphaltene-like molecules (polyaromatic hydrocarbons) in both aqueous and organic solvents by using a coarse-grained model known as the SAFT- γ Mie force field. This model relies on a top-down approach in which the force-field parameters are obtained using an equation of state. The use of solvation free energy calculations to evaluate this force field can help in improving the model and increasing the scale in which these simulations can be applied. The results presented here were obtained by carrying out molecular dynamics simulations, with the expanded ensemble method being applied to sample an alchemical path. For this, we employed a softcore variant of the Mie potential. From the output of these simulations, we estimated free-energy differences by means of the Multistate Bennett Acceptance Ratio method. The results with organic solvents exhibited low absolute deviations from experimental data. In turn, hydration free energy calculations required a binary interaction parameter to be estimated from molecular dynamics data, which displays a flaw in the top-down parameterization approach. Fortunately, however, a single binary parameter could be used for all pairs of polyaromatic hydrocarbons with water, proving that the SAFT- γ Mie force field exhibits a suitable transferability property that deserves further investigation. [ABSTRACT FROM AUTHOR]

[Display omitted] • The phase change and CO 2 absorption performance of [DETAH][Py]-solvent–water are screened and evaluated. • The difference of ΔG sol between water phase and DEDM phase calculated by the DFT illustrates the reason for the phase change. • FT-IR and 13C NMR indicate the formation of carbamates. • The acid hydrolysis shows that more than 90% of CO 2 products aggregate in the lower phase. Phase change absorbent can effectively reduce energy consumption in the process of CO 2 capture. In the present work, a series of CO 2 -capturing phase change absorbents consisting of diethyltriamine pyrazole ([DETAH][Py])-solvent–water have been studied. Among them, [DETAH][Py]-diethylene glycol dimethyl ether (DEDM)-water present good absorption and phase change performance. The experimental results showed that under the conditions of 303.15 K, C [DETAH][Py] = 1 mol/L, V DEDM:water = 4:6, the CO 2 absorption reached 1.515 mol CO 2 /mol IL, and the regeneration efficiency remained 90.75 % at 403.15 K. In addition, the absorption mechanism was explored based on FT-IR and 13C NMR. [DETAH][Py] captured CO 2 to generate carbamate, which was then partially hydrolyzed to HCO 3 −/CO 3 2−. In order to further explain the phase change mechanism, as to absorbed products, the difference of solvation free energy (ΔG sol) between water phase and DEDM phase was calculated by DFT. The results showed that the solubility of products in water is different from the solubility of DEDM, and most of products will dissolve in water, where the organic phase undergoes self-aggregation and is extruded out of the aqueous phase due to salting-out effect, causing phase change. [ABSTRACT FROM AUTHOR]

[Display omitted] • Machine-learning models are constructed for solvation and partition thermodynamics in ionic liquids. • Accuracy level reaches RMSE ∼ 0.2 kcal/mol and Pearson r ∼ 0.98. • The models could provide both solvation and partition thermodynamics with the same set of features. • The models are applicable to most ILs families and most gaseous and drug-like solutes. • Face-to-face comparison with alchemical free energy calculations suggests the superiority of the educated estimators. Ionic liquids (ILs) derivatives as novel green solvents are widely employed in laboratory and industrial applications. Many computational tools have been developed to compute various physiochemical properties of ILs species, e.g., mass density and viscosity. However, despite their central role as solvents, predictive tools for the solvation of external agents and the partition between water and ILs phases are rather limited. Common selections are atomistic simulations, especially the alchemical method. However, the relatively high computational costs could be harmful to large-scale applications. In this work, we develop a series of low-cost machine-learning estimators to exploit further the chemical-accuracy frontier. 1764 solvation and 1764 water-ILs transfer free energies involving 120 gaseous or drug-like solutes and many commonly applied ILs families are gathered from experimental references to form the dataset with a diverse coverage of chemical spaces. The best-performing tree-based method could predict simultaneously solvation and water-ILs biphasic partition thermodynamics with a state-of-the-art performance of ∼ 0.13 kcal/mol MAE, ∼0.25 kcal/mol RMSE, ∼0.98 Pearson r and ∼ 0.94 Kendall τ, beating transferable tools such as alchemical free energy calculations in a series of face-to-face comparisons. [ABSTRACT FROM AUTHOR]

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.

Fracturing in the form of foam fracturing is often involved in dealing with the exploitation of unconventional shale resources. On the other hand, Molecular Dynamics (MD) simulation has been developed progressively in the recent decades to provide the temporal and the spatial resolutions, which are experimentally unavailable. The MD simulation considers the atomic and the molecular interactions at microscopic and macroscopic levels. The objective of this research is to improve fluid interaction during hydraulic fracturing of the shale gas reservoirs. The potential parameters include the high reservoir pressure and high temperature (HPHT), the concentration of the surfactant, the presence of carbon dioxide (CO 2 ), and the brine salinity. The simulation focuses on the post effect of hydraulic fracturing on the shale system after the fracturing process has had taken place whereby the CO 2 is introduced into the shale gas system, which consists of methane (CH 4 ) with brine and surfactants applied during the foam fracturing is Alfa Olefin Sulfonate (AOS). Different brine salinity, temperature, and pressure are varied in the simulation to determine the most spontaneous combination for the reservoir condition for the solvation of CO 2 in the shale fluid system in the reflection of solvation free energy. The simulation result is validated with the experimental study, which is performed to determine the critical micelle concentration (CMC) value of AOS solution in the two-phase fluid consisting of CO 2 and brine. The result shows that the most spontaneous system happens at the condition of 423.15 K and 30,000 ppm salinity. The pressure is fairly insignificant to the output of solvation free energy. The larger the negativity of the solvation free energy indicates that the more spontaneous of the CO 2 in the interaction with the surrounding particles in the system. This research is beneficial in the future prediction of the behavior and the spontaneity of the CO 2 in the shale gas recovery. Apart from that, the CMC in simulation and experimental results are 0.014 wt% and 0.016 wt% which proves that the simulation result is valid. [ABSTRACT FROM AUTHOR]

Implicit solvent models divide solvation free energies into polar and nonpolar additive contributions, whereas polar and nonpolar interactions are inseparable and nonadditive. We present a feature functional theory (FFT) framework to break this ad hoc division. The essential ideas of FFT are as follows: (i) representability assumption: there exists a microscopic feature vector that can uniquely characterize and distinguish one molecule from another; (ii) feature-function relationship assumption: the macroscopic features, including solvation free energy, of a molecule is a functional of microscopic feature vectors; and (iii) similarity assumption: molecules with similar microscopic features have similar macroscopic properties, such as solvation free energies. Based on these assumptions, solvation free energy prediction is carried out in the following protocol. First, we construct a molecular microscopic feature vector that is efficient in characterizing the solvation process using quantum mechanics and Poisson-Boltzmann theory. Microscopic feature vectors are combined with macroscopic features, that is, physical observable, to form extended feature vectors. Additionally, we partition a solvation dataset into queries according to molecular compositions. Moreover, for each target molecule, we adopt a machine learning algorithm for its nearest neighbor search, based on the selected microscopic feature vectors. Finally, from the extended feature vectors of obtained nearest neighbors, we construct a functional of solvation free energy, which is employed to predict the solvation free energy of the target molecule. The proposed FFT model has been extensively validated via a large dataset of 668 molecules. The leave-one-out test gives an optimal root-mean-square error (RMSE) of 1.05 kcal/mol. FFT predictions of SAMPL0, SAMPL1, SAMPL2, SAMPL3, and SAMPL4 challenge sets deliver the RMSEs of 0.61, 1.86, 1.64, 0.86, and 1.14 kcal/mol, respectively. Using a test set of 94 molecules and its associated training set, the present approach was carefully compared with a classic solvation model based on weighted solvent accessible surface area. © 2017 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

We investigate the energetics of the antiparallel, hybrid and parallel type G-quadruplex structures of the human telomere DNA sequence. We find that both the conformational energy and solvation free energy of these structures are roughly inversely proportional to their radii of gyration. We rationalize this finding in terms of the dominance of the electrostatic contributions. We also show that the solvation free energy is more significant than the conformational energy in determining the G-quadruplex structures, which is in contrast to the canonical B-DNA structures. Our work will contribute to an understanding of the molecular mechanisms dictating various G-quadruplex topologies. [ABSTRACT FROM AUTHOR]

Predicting values of the solvation free energy of ionic solutes is essential in chemical and biological processes as this property is required to model kinetics, electrochemical potentials, and acidity constants; however, developing computational prediction methods is challenging, partly because of the lack of accurate reference data. The state-of-the-art Minnesota solvation database reports an uncertainty of 3.0 kcal mol−1 in the absolute solvation free energy of ionic solutes, which is large for further method development and validation. To reduce this uncertainty, we have created an accurate reference database, the database of ionic solutes' solvation free energies (DISSOLVE). It consists of 330 ionic solvation free energy data entries of various chemical classes in water, dimethyl sulfoxide, methanol, and acetonitrile. The solvation free energy of these ionic solutes is determined by combining measured and computed values of the physical properties. We have extracted aqueous solvation energies of neutral solutes and pK a values from experimental studies in the literature. Using ab initio methods, we compute the gas phase acidity and non-aqueous solvation energy values of neutral solutes. On the basis of our analysis, we find that DISSOLVE entries are more accurate than the previously reported reference data in the literature, achieving a lower uncertainty of 1.5 kcal mol−1 for aqueous and 2.6 kcal mol−1 for non-aqueous absolute solvation free energies of ionic solutes. Additionally, we find an uncertainty of 1.0 kcal mol−1 and 1.1 kcal mol−1 for the conventional solvation free energies of ionic solutes in aqueous and non-aqueous solutions, respectively. With this improvement, our database enables the prediction of equilibrium and reaction rate constants within an order of magnitude, as well as the development and validation of new computational chemistry approaches for predicting the solvation free energy of ionic solutes. [Display omitted] • Database of 330 ionic solvation free energy data entries of various chemical classes. • Uncertainty of 1.0/1.1 kcal mol−1 (conventional) for aqueous/non-aqueous solutions. • Uncertainty of 1.5/2.6 kcal mol−1 (absolute) for aqueous/non-aqueous solutions. • The DISSOLVE data entries can be found at http://www.ltt.rwth-aachen.de/dissolve. [ABSTRACT FROM AUTHOR]

We have studied the accuracy of Ornstein-Zernike (OZ) integral equation theory in terms of the solvation free energy (SFE) for a two-component Lennard-Jones system at the infinite dilution limit. We have employed the hypernetted chain (HNC), Kovalenko-Hirata (KH), Kobryn-Gusarov-Kovalenko (KGK), and Percus-Yevick (PY) closure equations. Further, we have extended the Verlet-modified (VM) closure to the two-component system to examine the accuracy of this method in terms of the SFE. Molecular dynamics simulations were employed to compare the results with the above mentioned OZ theories. The HNC and KH approximations significantly overestimate the SFE, whereas the PY approximation tends to underestimate it. The overestimation of the SFE by the KGK approximation becomes significant when the solvent density is relatively high. In contrast, the VM approximation is found to be rather accurate at all studied conditions. An analysis of the integrand for the SFE reveals that, to improve the SFE, the first rising (FR) region in the radial distribution function (RDF) must be corrected. We have also tested the sigma-enlarging-bridge (SEB) function that we proposed previously [T. Miyata and Y. Ebato, J. Mol. Liq. 217 (2016) 75] for the correction of the FR region of the RDF. In this study, we applied the SEB function to both HNC-type and KH-type closures and found that these combinations (SEB-HNC and SEB-KH) improve the SFE significantly. [ABSTRACT FROM AUTHOR]

In a recent work, we performed free-energy analyses for hydration of benzene by conducting QM/MMER simulations, where the total solvation free energy Δμ was decomposed into contributions Δ... and δμ(Δμ = Δ... + δμ). Δ... is the solvation free energy of the solute with a fixed electron density and δμ is the residual free energy due to the electron density polarisation in solution. We, further, decomposed the free energies δμ due to electron density fluctuations in aromatic solutes in aqueous solutions into contributions from π and σ orbitals. We note, however, that the decompositions will not be validated when the polarisations of π orbitals seriously couple with those of σ orbitals. In this paper, we study a correlation matrix between polarisations of π and σ orbitals through QM/MM simulations to assess the coupling strength among the orbitals. We found that the electron density polarisation is dominated by the polarisation arising from π - π∗ transfers between the orbitals lying in the HOMO-LUMO region. Thus, the polarisation of π electrons hardly couples with that from σ orbitals, which justifies our decomposition analyses. [ABSTRACT FROM AUTHOR]

The study of solvation of three phenolic acids, 3,4,5-trihydroxybenzoic acid (gallic acid), 3,4-dihydroxybenzoic acid (protocatechuic acid), and 4-hydroxy-3,5-dimethoxybenzoic acid (syringic acid), in pure, methanol-modified and acetone-modified (0.03 and 0.06 mol fraction) supercritical (SC) carbon dioxide has been carried out by computational methods. Structural features of hydrogen-bonded complexes formation in modified SC media have been researched by using classical molecular dynamics at different densities corresponding to the experimental pressures 20 and 30 MPa and temperatures from 313 to 333 K. The results obtained have revealed that an increase of fluid density and cosolvent concentration provide increasing the average number of hydrogen bonds formed by the solute with cosolvent. Ab initio calculations of formation energy of hydrogen-bonded complexes solute – cosolvent have been performed. Solvation free energy of the phenolic acids in SC CO 2 with and without cosolvents was evaluated by means of the Bennett’s acceptance ratio method. The solvation free energy has been found to be strongly dependent on the number of hydrogen bonds solute – cosolvent. Cosolvent-induced solubility enhancement values were also calculated. The results indicate that the increase in solubility of the phenolic acids is observed by passing from the pure SC CO 2 through acetone-modified to methanol-modified solvent. Moreover the order of cosolvent-induced solubility enhancement magnitude depends on the number of hydrogen bonds solute – cosolvent and greatly on the concentration of the latter. [ABSTRACT FROM AUTHOR]

The solvation free energies of five nucleic acid bases in [Cnbim]Br (wheren = 2, 4, 6) ionic liquids (ILs) were computed using the Bennett acceptance ratio (BAR) method employing molecular dynamics simulations. The computed free energies using BAR were in agreement with other methods. The large and negative predicted free energies of the bases in ILs indicated that the bases were better solvated in the ILs rather than in water. Hydrogen bonding interactions between polar sites of the bases and ILs’ ions significantly contributed to the solvation mechanism. [ABSTRACT FROM AUTHOR]

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All-atom molecular dynamics simulations were used to predict water-cyclohexane distribution coefficients $$D_{cw}$$ of a range of small molecules as part of the SAMPL5 blind prediction challenge. Molecules were parameterized with the transferable all-atom OPLS-AA force field, which required the derivation of new parameters for sulfamides and heterocycles and validation of cyclohexane parameters as a solvent. The distribution coefficient was calculated from the solvation free energies of the compound in water and cyclohexane. Absolute solvation free energies were computed by an established protocol using windowed alchemical free energy perturbation with thermodynamic integration. This protocol resulted in an overall root mean square error in $$\log D_{cw}$$ of almost 4 log units and an overall signed error of −3 compared to experimental data. There was no substantial overall difference in accuracy between simulating in NVT and NPT ensembles. The signed error suggests a systematic error but the experimental $$D_{cw}$$ data on their own are insufficient to uncover the source of this error. Preliminary work suggests that the major source of error lies in the hydration free energy calculations. [ABSTRACT FROM AUTHOR]

The performance of the extended solvent-contact model has been addressed in the SAMPL5 blind prediction challenge for distribution coefficient (LogD) of drug-like molecules with respect to the cyclohexane/water partitioning system. All the atomic parameters defined for 41 atom types in the solvation free energy function were optimized by operating a standard genetic algorithm with respect to water and cyclohexane solvents. In the parameterizations for cyclohexane, the experimental solvation free energy (Δ G ) data of 15 molecules for 1-octanol were combined with those of 77 molecules for cyclohexane to construct a training set because Δ G values of the former were unavailable for cyclohexane in publicly accessible databases. Using this hybrid training set, we established the LogD prediction model with the correlation coefficient ( R), average error (AE), and root mean square error (RMSE) of 0.55, 1.53, and 3.03, respectively, for the comparison of experimental and computational results for 53 SAMPL5 molecules. The modest accuracy in LogD prediction could be attributed to the incomplete optimization of atomic solvation parameters for cyclohexane. With respect to 31 SAMPL5 molecules containing the atom types for which experimental reference data for Δ G were available for both water and cyclohexane, the accuracy in LogD prediction increased remarkably with the R, AE, and RMSE values of 0.82, 0.89, and 1.60, respectively. This significant enhancement in performance stemmed from the better optimization of atomic solvation parameters by limiting the element of training set to the molecules with experimental Δ G data for cyclohexane. Due to the simplicity in model building and to low computational cost for parameterizations, the extended solvent-contact model is anticipated to serve as a valuable computational tool for LogD prediction upon the enrichment of experimental Δ G data for organic solvents. [ABSTRACT FROM AUTHOR]

Alchemical transformation of solutes using classical fixed-charge force fields is a popular strategy for assessing the free energy of transfer in different environments. Accurate estimations of transfer between phases with significantly different polarities can be difficult because of the static nature of the force fields. Here, we report on an application of such calculations in the SAMPL5 experiment that also involves an effort in balancing solute and solvent interactions via their expected static dielectric constants. This strategy performs well with respect to predictive accuracy and correlation with unknown experimental values. We follow this by performing a series of retrospective investigations which highlight the potential importance of proper balancing in these systems, and we use a null hypothesis analysis to explore potential biases in the comparisons with experiment. The collective findings indicate that considerations of force field compatibility through dielectric behavior is a potential strategy for future improvements in transfer processes between disparate environments. [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. [ABSTRACT FROM AUTHOR]