Solvation Free Energy Calculations Research Articles

A Machine Learning Free Energy Functional for the 1D Reference Interaction Site Model: Towards Prediction of Solvation Free Energy for All Solvent Systems

Understanding the interactions between solutes and solvents is vital in many areas of the chemical sciences. Solvation free energy (SFE) is an important thermodynamic property in characterising molecular solvation and so accurate prediction of this property is sought after. The One-Dimensional Reference Interaction Site Model (RISM) is a well-established method for modelling solvation, but it is known to yield large errors in the calculation of SFE. In this work, we show that a single machine learning free energy functional for RISM can accurately model solvation thermodynamics in multiple solvents. A convolutional neural network is trained on solvation free energy density functions calculated by RISM for small organic molecules in approximately 100 different solvent systems. We achieve an average RMSE of 1.41 kcal/mol and an R2 of 0.89 across all solvent systems. We also compare the performance for the most and least commonly represented solvents and show that higher accuracy is generally seen with higher volumes of data, with RMSE values of 0.69–1.29 kcal/mol and R2 values of 0.78–0.97 for solvents with more than 50 data points. We have shown that machine learning can greatly improve solvation free energy predictions in RISM, while demonstrating that the methodology is generalisable across solvent systems. This represents a significant step towards a universal machine learning SFE functional for RISM.

Hydration behavior of D-calcium pantothenate (vitamin B5) in the presence of sugar-based deep eutectic solvents at different temperatures: experimental and theoretical study

Considerable efforts have been devoted in recent years to enhancing the efficacy medicinal substance, leading to the discovery of innovative drug formulations and delivery techniques. The successful design of these processes necessitates a profound understanding at the molecular level of how these substances interact with biological membranes. Thorough thermodynamic investigations provide invaluable insights into these interactions and aid in selecting suitable compounds for pharmaceutical production. This study aims to determine the density and speed of sound for D-calcium pantothenate in mixtures of water and deep eutectic solvents (DESs), specifically choline chloride/sucrose, choline chloride/ glucose, and choline chloride/ fructose (with 2:1 molar ratio) over a temperature range of 288.15 K to 318.15 K under atmospheric pressure. In order to predict the behavior of molecules, COSMO model (the Conductor-Like Screening Model) offer complementary strengths in quantum chemistry. This approach allows for calculating solvation free energies, making it ideal for predicting properties like solubility, where understanding solvent-solute interactions is crucial. By correlating the measured parameters using standard relationships, important partial molar parameters such as apparent molar volumes and apparent molar isentropic compressibility are calculated. Additionally, apparent molar isobaric expansion, and Hepler’s constant are derived from the density and speed of sound data. The experimental apparent molar volumes, and apparent molar isentropic compressibility data is fitted to the Redlich-Meyer equation to obtain significant quantities such as standard partial molar volume, and partial molar isentropic compression. The comprehensive thermodynamic analysis of this studied system holds immense significance for advancements in the pharmaceutical industry.

Multiparameter Method for Developer Screening of Molecular Glass Resists via Molecular Simulations.

Efficient and accurate developer screening is critical for high-resolution lithography. In this study, a multiparameter (MP) method simultaneously based on the Hansen solubility parameters (HSP), two-component solubility parameters (TSP), and order parameter (OP) is proposed for the developer screening of molecular glass resists via molecular dynamics simulation. A customized solvent database, including 80 organic solvents, is created for the subsequent developer screening. Two diagram forms of HSP (δD-δP-δH diagram and δV-δH diagram) were investigated, and the δV-δH diagram is utilized due to its higher solubility prediction consistency with OP. Then, two solubility prediction diagrams, i.e., HSP&OP and TSP&OP diagrams, were formed using the MP method. The developer screening scheme using the MP method is illustrated for a decomposable resist, AD10BOC, and a cross-linkable resist, AD4C. Meanwhile, the feasibility of the developer screening scheme is verified by practical lithography experiments. In addition, relative solvation free energy (RSFE) calculations and dissolution experiments of six resists are implemented to further confirm the accuracy of OP in reflecting the relative solubility of the resist in various solvents. This study presents a viable approach for developer screening, which will facilitate the development of resist materials.

Replica Exchange of Expanded Ensembles: A Generalized Ensemble Approach with Enhanced Flexibility and Parallelizability.

Generalized ensemble methods such as Hamiltonian replica exchange (HREX) and expanded ensemble (EE) have been shown effective in free energy calculations for various contexts, given their ability to circumvent free energy barriers via nonphysical pathways defined by states with different modified Hamiltonians. However, both HREX and EE methods come with drawbacks, such as limited flexibility in parameter specification or the lack of parallelizability for more complicated applications. To address this challenge, we present the method of replica exchange of expanded ensembles (REXEE), which integrates the principles of HREX and EE methods by periodically exchanging coordinates of EE replicas sampling different yet overlapping sets of alchemical states. With the solvation free energy calculation of anthracene and binding free energy calculation of the CB7-10 binding complex, we show that the REXEE method achieves the same level of accuracy in free energy calculations as the HREX and EE methods, while offering enhanced flexibility and parallelizability. Additionally, we examined REXEE simulations with various setups to understand how different exchange frequencies and replica configurations influence the sampling efficiency in the fixed-weight phase and the weight convergence in the weight-updating phase. The REXEE approach can be further extended to support asynchronous parallelization schemes, allowing looser communications between larger numbers of loosely coupled processors such as cloud computing and therefore promising much more scalable and adaptive executions of alchemical free energy calculations. All algorithms for the REXEE method are available in the Python package ensemble_md, which offers an interface for REXEE simulation management without modifying the source code in GROMACS.

Ion solvation free energy calculations based on first-principles molecular dynamics thermodynamic integration.

Numerous electrochemistry reactions require the precise calculation of the ion solvation energy. Despite the significant progress in the first-principles calculations for crystals and defect formation energies for solids, the liquid system free energy calculations still face many challenges. Ion solvation free energies can be calculated via different semiempirical ways, e.g., using implicit solvent models or cluster of explicit molecule models; however, systematically improving these models is difficult due to their lack of a solid theoretical base. A theoretically sound approach for calculating the free energy is to use thermodynamic integration. Nevertheless, owing to the difficulties of self-consistent convergence in first-principles calculations for unphysical atomic configurations, the computational alchemy approach has not been widely used for first-principles calculations. This study proposes a general approach to use first-principles computational alchemy for calculating the ion solvation energy. This approach is also applicable for other small molecules. The calculated ion solvation free energies for Li+, Na+, K+, Be2+, Mg2+, and Ca2+ are close to the experimental results, and the standard deviation due to molecular dynamics fluctuations is within 0.06eV.

Sequence-dependent dipeptide solubility in ethanol-water and DMSO-water solutions

Reliable solubility data is crucial for controlling and optimising crystallisation conditions, to provide a cost-effective purification method with the potential for scaling up. This study investigated the effect of amino acid sequence on determining peptides solubility behaviours. Four dipeptides with different sequences and combinations of alanine and glycine were selected to study their temperature dependent (283.15 K to 313.15 K) aqueous solubilities and solubility in the presence of ethanol and DMSO as antisolvent at 298.15 K. Aqueous solubility data of dipeptides presents the following order: glycyl-L-alanine (gly-ala) > L-alanyl-L-alanine (ala-ala) > glycyl glycine (gly-gly) > L-alanyl glycine (ala-gly). This solubility order has been substantiated by solvation free energy calculations using molecular dynamics (MD) simulations, providing insights into the underlying molecular interactions that govern these solubility patterns. The solubility data also demonstrated difference in the temperature dependency that: gly-gly > gly-ala > ala-gly > ala-ala, corresponding to their dissolution enthalpy order. In the presence of antisolvent range from 0 % to 80 % (by mass) concentration, peptide’s solubility was more susceptible to DMSO than ethanol. Accurate prediction of the solubilities validated the effectiveness of non-random two-liquid (NRTL) model for peptide studies.

Predicting Solvation Free Energies from the Minnesota Solvation Database Using Classical Density Functional Theory Based on the PC-SAFT Equation of State.

We critically assess the capabilities of classical density functional theory (DFT) based on the perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state to predict the solvation free energies of small molecules in various hydrocarbon solvents. We compare DFT results with experimental data from the Minnesota solvation database and utilize statistical methods to analyze the accuracy of our approach, as well as its weaknesses. The mean absolute error of the solvation free energies is 3.7 kJ mol-1 for n-alkane solvents, ranging from pentane to hexadecane, with 473 solute-solvent systems. For solvents consisting of cyclic hydrocarbons (cyclohexane, benzene, toluene, and ethylbenzene) with 245 solute-solvent systems, we report a slightly larger mean absolute error of 4.2 kJ mol-1. We identify three possible sources of errors: (i) the neglect of solute-solvent and solvent-solvent Coulomb interactions, which limits the applicability of PC-SAFT DFT to nonpolar and weakly polar molecules; (ii) the solute's Lennard-Jones parameters supplied by the general AMBER force field, which are not parametrized toward solvation free energies; and (iii) the application of the Lorentz-Berthelot combining rules to the dispersive interactions between a segment of the PC-SAFT solvent and a Lennard-Jones interaction site of the solute. The approach is more accurate than standard implementations of phenomenological models in common chemistry software packages, which exhibit mean absolute errors larger than 9.12 kJ mol-1, even though newer phenomenological models achieve a mean absolute error of about 2 kJ mol-1. PC-SAFT DFT is more computationally efficient than state of the art explicit molecular simulations in combination with free energy perturbation methods. It is predictive with respect to solvation free energies, i.e., the input for the model is the (element-specific) molecular force field, the solute configuration from molecular dynamics simulations, and the (substance-specific) PC-SAFT parameters. The PC-SAFT parametrization uses pure-component data and does not require experimental solvation free energies. The PC-SAFT equation of state, without applying a DFT formalism, can also be used to calculate solvation free energies, provided that the PC-SAFT parameters for the solute are available. A large number of substances was recently parametrized by members of our group (Esper, T.; Bauer, G.; Rehner, P.; Gross, J. Ind. Eng. Chem. Res. 2023, 62), which enables a comparison to the DFT approach for 103 substances. Accurate results are obtained from the PC-SAFT equation of state with an MAE below 2.51 kJ mol-1. The DFT approach does not require PC-SAFT parameters for the solute and can be applied to all solutes that can be represented by the molecular force field.

Acceleration of Solvation Free Energy Calculation via Thermodynamic Integration Coupled with Gaussian Process Regression and Improved Gelman-Rubin Convergence Diagnostics.

The determination of the solvation free energy of ions and molecules holds profound importance across a spectrum of applications spanning chemistry, biology, energy storage, and the environment. Molecular dynamics simulations are powerful tools for computing this critical parameter. Nevertheless, the accurate and efficient calculation of the solvation free energy becomes a formidable endeavor when dealing with complex systems characterized by potent Coulombic interactions and sluggish ion dynamics and, consequently, slow transition across various metastable states. In the present study, we expose limitations stemming from the conventional calculation of the statistical inefficiency g in the thermodynamic integration method, a factor that can hinder the determination of convergence of the solvation free energy and its associated uncertainty. Instead, we propose a robust scheme based on Gelman-Rubin convergence diagnostics. We leverage this improved estimation of uncertainties to introduce an innovative accelerated thermodynamic integration method based on the Gaussian Process regression. This methodology is applied to the calculation of the solvation free energy of trivalent rare-earth elements immersed in ionic liquids, a scenario in which the aforementioned challenges render standard approaches ineffective. The proposed method proves to be effective in computing solvation free energy in situations where traditional thermodynamic integration methods fall short.

Solubility measurements and molecular dynamics simulations of perphenazine (form I) in three binary solvent systems

This study aimed to investigate the perphenazine (form I)’s solubility in three binary solvent systems: acetone + water, n-propanol + water, and N,N-dimethylformamide (DMF) + water, within the temperature range of 278.15 K to 318.15 K. The solubility data were obtained using a dynamic method and fitted using four models: the NRTL model, modified Apelblat equation, CNIBS/R-K model, and Jouyban-Acree model. All four models provided satisfactory fitting results, with the CNIBS/R-K model exhibiting the best performance. In all cases, it was observed that the solubility of perphenazine (form I) exhibited a positive correlation with temperature while keeping the solvent composition constant. Specifically, in the DMF + water binary solvent system, the solubility of perphenazine (form I) increased with higher mass fractions of the positive solvent. Additionally, the phenomenon of co-solvency was observed when perphenazine (form I) was dissolved in the acetone + water and n-propanol + water binary solvent systems. To gain insights into the intermolecular interactions within the perphenazine (form I) crystal, Hirshfeld surface (HS) analysis was employed. The Dmol3 module was used to calculate the electrostatic potential of perphenazine molecules, followed by molecular dynamics simulation, analysis of the solute–solvent and solvent–solvent radial distribution functions (RDF), and calculation of solvation free energy. Overall, this study enhances the understanding of the dissolution behavior of perphenazine (form I) and provides valuable data to support further investigations into its crystallization process.

Binding Free Energy Calculation Based on the Fragment Molecular Orbital Method and Its Application in Designing Novel SHP-2 Allosteric Inhibitors.

The accurate prediction of binding free energy is a major challenge in structure-based drug design. Quantum mechanics (QM)-based approaches show promising potential in predicting ligand-protein binding affinity by accurately describing the behavior and structure of electrons. However, traditional QM calculations face computational limitations, hindering their practical application in drug design. Nevertheless, the fragment molecular orbital (FMO) method has gained widespread application in drug design due to its ability to reduce computational costs and achieve efficient ab initio QM calculations. Although the FMO method has demonstrated its reliability in calculating the gas phase potential energy, the binding of proteins and ligands also involves other contributing energy terms, such as solvent effects, the 'deformation energy' of a ligand's bioactive conformations, and entropy. Particularly in cases involving ionized fragments, the calculation of solvation free energy becomes particularly crucial. We conducted an evaluation of some previously reported implicit solvent methods on the same data set to assess their potential for improving the performance of the FMO method. Herein, we develop a new QM-based binding free energy calculation method called FMOScore, which enhances the performance of the FMO method. The FMOScore method incorporates linear fitting of various terms, including gas-phase potential energy, deformation energy, and solvation free energy. Compared to other widely used traditional prediction methods such as FEP+, MM/PBSA, MM/GBSA, and Autodock vina, FMOScore showed good performance in prediction accuracies. By constructing a retrospective case study, it was observed that incorporating calculations for solvation free energy and deformation energy can further enhance the precision of FMO predictions for binding affinity. Furthermore, using FMOScore-guided lead optimization against Src homology-2-containing protein tyrosine phosphatase 2 (SHP-2), we discovered a novel and potent allosteric SHP-2 inhibitor (compound 8).

An atomic surface site interaction point description of non-covalent interactions.

Molecular electrostatic potential surfaces (MEPS) calculated using density functional theory have been used to develop a simplified description of the non-covalent interaction properties of organic molecules. The Atomic Interaction Point (AIP) model introduced here represents an evolution of the Surface Site Interaction Point (SSIP) model described previously, in which a molecule is represented by a discrete set of interaction points that define sites of interaction with other molecules. The interaction sites are described by interaction parameters that are equivalent to the experimentally determined H-bond donor and acceptor parameters α and β. By using high electron density MEPS that lie inside the van der Waals surface, it is possible to obtain accurate interaction parameters and locations for polar sites (s-holes, H-bond donors and acceptors), which are identified as local maxima and minima on the MEPS. For non-polar sites that represent π-systems and halogens, an approach based on molecular orbitals was used to assign the locations of the AIPs, and the interaction parameters were obtained using a lower electron density MEPS that lies close to the van der Waals surface. The AIP descriptions can be implemented directly in the Surface Site Interaction Point Model for Liquids at Equilibrium (SSIMPLE) to calculate solvation free energies, and the free energy of transfer of 1504 compounds from n-hexadecane to water was predicted with a root mean square error of 5 kJ mol-1. AIPs also provide a useful tool for mapping non-covalent interactions in intermolecular complexes, and examples are provided showing how X-ray crystal structures can be converted into AIP interaction maps that allow quantification of the free energy contributions of both polar and non-polar interactions to the stabilities of complexes in solution.

Three pesticide-pesticide eutectic systems of boscalid: Mechanochemical preparation, characterization and mechanism analysis

Preparation of eutectic mixture is considered to be an efficient means to improve physical and chemical properties in many fields. In this work, three pesticide-pesticide eutectic systems of boscalid with diniconazole, uniconazole and 3,6-dichloropicolinic acid were prepared by mechanochemical method. The formation of eutectic mixtures was identified by differential scanning calorimetry and powder X-ray diffraction characterizations. From the constructed solid–liquid phase diagrams, it is discovered that these eutectic systems could form a eutectic when the molar fraction of boscalid is 0.500. In addition, all eutectic temperatures are more than 29 K lower than the higher melting points of the constituents in the systems, which eliminates the issue of the poor thermal stability of constituents and enables the treatment of them using melting-based operations. Ideal model and COSMO-RS theory also calculate these solid–liquid phase diagrams relatively accurately, and the calculated activity coefficient values prove that these systems do not deviate significantly from the ideal state. The distinct non-ideality at the eutectic composition of the three systems also suggests that the interactions of the three coformers and the boscalid differ. The formation of eutectic mixtures was further confirmed by other solid state characterizations including mid-infrared spectroscopy, Raman spectroscopy and etc. The accelerated stability test indicates the good stability of the three eutectic mixtures. Furthermore, the water solubility study suggests the boscalid in the eutectic systems has a higher solubility than raw boscalid. By using COSMO-RS modelling, the most typical homo/hetero-molecular complexes of these pesticides were obtained and analyzed. Based on the trend of affinity characterized by the Gibbs free energy of the generation of molecular complexes, it could be inferred that eutectic point could be viewed as the state for which affinity of hetero-molecular complexes is greatest. The affinity of hetero-molecular complexes also presents the interaction between boscalid and 3,6-dichloropicolinic acid is greater than the other two pesticides at the eutectic point. Furthermore, the calculated solvation free energies document the intermolecular interaction plays an important role in stabilizing the three eutectic systems. These eutectic systems solve the problem that the three coformers are unsuitable for melting-based procedures and improve the solubility, making them potential options for future pesticide formulation development.

The dissolution mechanism, molecular dynamics simulation and thermodynamic analysis of nirmatrelvir (treatment of COVID-19) in aqueous ethanol mixtures

This research investigates the cosolvent effect of nirmatrelvir (treatment of COVID-19) in ethanol + water mixtures. The result shows that the maximum solubility in mole fraction is 8.313 × 10−2 at the ethanol mass fraction of 80%, which is greater than that in pure water. The solvent effect analysis indicates that the hydrogen bond acidity (HBA) of solvent has the greatest impact on the solubility of nirmatrelvir solubility. The negative preferential solvation values in water-rich regions indicate that nirmatrelvir molecules are preferentially solvated by water molecules. With the rising mass fraction of cosolvent, ethanol disrupts the ordered structure of water molecules in solution through hydrogen bonding, allowing nirmatrelvir molecules to be preferentially solvated by ethanol. Nonetheless, nirmatrelvir molecules are preferentially solvated by water again in ethanol-rich regions. Through molecular dynamics calculations (MD), it could be found that the dissolution behavior is consistent with its calculated by solvation free energy calculation. Furthermore, the tall and sharp peak of the RDF plots of O···H-O (ethanol/water) and N-H···O (ethanol/water) were attained at 1.50–2.50 Å, indicating that ethanol and water molecules form a regular and definite coordination sphere around the nirmatrelvir moiety at the distance of 1.50–2.50 Å through hydrogen bonding form. Additionally, the correlation fitting was performed for the solubility data through the Jouyban-Acree model and its derivative models. Moreover, “Apparent thermodynamic analysis” of nirmatrelvir dissolution process in aqueous ethanol mixtures including entropy, enthalpy and Gibbs free energy were completed.

Dense Neural Network for Calculating Solvation Free Energies from Electronegativity-Equalization Atomic Charges.

I propose a dense Neural Network for evaluation of solvation free energies ΔG°solv for molecules and ions in water and nonaqueous solvents, Easy Solvation Energy with Electronegativity Equalization charges and Dense Neural Network (ESE-EE-DNN). As input features, it uses the Conductor-like Screening Model (COSMO) electrostatic energy, atomic cavity surface areas, total cavity volume, and induced surface charges. For the COSMO calculation, electronegativity-equalization atomic charges are employed. ESE-EE-DNN exhibits fairly high accuracy, similar or even superior to that of mainstream density functional theory-based methods. For neutral solutes in water, polar protic, polar aprotic, and nonpolar solvents, ESE-EE-DNN yields a root-mean-square error (RMSE) of 1.25, 1.36, 0.70, and 0.71 kcal/mol, respectively. ESE-EE-DNN is particularly advantageous for ionic solutes, with an RMSE of 2.82 and 1.42 kcal/mol for aqueous and nonaqueous ion solutions, correspondingly. ESE-EE-DNN is very efficient due to a fast evaluation of the electronegativity-equalization charges.

Molecular simulations of understanding the Na+ ion structure, dynamic and thermodynamic behavior in ionic liquids: Butyl ammonium hydrogen bisulfate and tri-butyl ammonium hydrogen bisulfate

This manuscript presents the all-atom molecular dynamics simulations to investigate intermolecular structure and solvation thermodynamics of Na+ ion in two different ammonium-based protic ionic liquids (1) Butyl Ammonium hydrogen bisulfate [BA+][HSO4−], (2) Tri-butyl ammonium hydrogen bisulfate [TBA+][HSO4−]. The ionic liquid [BA+][HSO4−] show a more coordinated behavior when compared to [TBA+][HSO4−], which is observed over the temperature range from 278 K to 348 K. Hydrogens of the cations show a hydrogen bonding interaction with oxygens of anions. The cationic [TBA+] molecules show more solvation behavior with anions when compared to the [BA+]. The Na+ ion show a strong coordination structure with [HSO4−] in [TBA+][HSO4−] when compared to the [BA+][HSO4−]. We further calculate the detailed solvation free energy (ΔG) calculations using thermodynamic integration. We found that the ΔG of Na+ is more favorable in [TBA+][HSO4−] when compared to [BA+][HSO4−] in the temperature range varying from 278 K to 348 K. With the temperature rise, we observe the more favorable solvation of Na+ in both ionic liquids. On the other hand, the solvation of Cl− becomes less favorable. Overall, this manuscript provides detailed molecular level structural and thermodynamic origins of Na+ in protic ionic liquids useful for designing and developing sustainable electrolytes for Na+ battery applications.

Solv: An Alternative Continuum Model Implementation Based on Fixed Atomic Charges, Scaled Particle Theory, and the Atom-Atom Potential Method.

An alternative continuum model implementation is reported. The electrostatic contribution to the solvation Gibbs free energy utilizes the noniterative conductor-like screening model of Vyboishchikov and Voityuk (DOI: 10.1002/jcc.26531) based on the fixed partial atomic charges. The nonelectrostatic solute-solvent dispersion-repulsion energy is calculated through the Caillet-Claverie atom-atom potential method employing the grid-based approach. The nonelectrostatic cavitation energy is computed within the scaled particle theory (SPT) formalism with the solute hard-sphere radius obtained via the Pierotti-Claverie (PC) scheme, from the solute molecular surface (SPT-S) or volume (SPT-V). The solvent hard-sphere radius is derived through the fitting to the experimental total solvation free energies of 2530 neutral species in 92 solvents. Application of the model to reproduce both the absolute and relative (reaction net) solvation free energies indicates that the SPT-V approach based on the CM5 charges is the best performer. The method is suggested for the solvation free energy calculation in the nonaqueous solvents.

In silico mutagenesis-based designing of oncogenic SHP2 peptide to inhibit cancer progression

Cancer is among the top causes of death, accounting for an estimated 9.6 million deaths in 2018, it appeared that approximately 500,000 people die from cancer in the United States alone annually. The SHP2 plays a major role in regulation of cell growth, proliferation, and differentiation, and functional upregulation of this enzyme is linked to oncogenesis and developmental disorders. SHP2 activity has been linked to several cancer types for which no drugs are currently available. In our study, we aimed to design peptide inhibitors against the SHP2 mutant. The crystal structure of the human Src SH2-PQpYEEIPI peptide mutant was downloaded from the protein databank. We generated several peptides from the native wild peptide using an in silico mutagenesis method, which showed that changes (P302W, Y304F, E306Q, and Q303A) might boost the peptide's affinity for binding to SHP2. Furthermore, the dynamical stability and binding affinities of the mutated peptide were confirmed using Molecular dynamics simulation and Molecular Mechanics with Generalized Born and Surface Area Solvation free energy calculations. The proposed substitution greatly enhanced the binding affinity at the residue level, according to a study that decomposed energy into its component residues. Our proposed peptide may prevent the spread of cancer by inhibiting SHP2, according to our detailed analyses of binding affinities.

Development and test of highly accurate endpoint free energy methods. 1: Evaluation of ABCG2 charge model on solvation free energy prediction and optimization of atom radii suitable for more accurate solvationfreeenergy prediction by the PBSA method.

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.

Interactions of Sodium Salicylate with β-Cyclodextrin and an Anionic Resorcin[4]arene: Mutual Diffusion Coefficients and Computational Study.

The interaction between sodium salicylate (NaSal) and the two macrocycles 5,11,17,23-tetrakissulfonatomethylene-2,8,14,20-tetra(ethyl)resorcinarene (Na4EtRA) and β-cyclodextrin (β-CD) has been studied by the determination of ternary mutual diffusion coefficients, and spectroscopic and computational techniques. The results obtained by the Job method suggest that the complex formation is given in a 1:1 ratio for all systems. The mutual diffusion coefficients and the computational experiments have shown that the β-CD-NaSal system presents an inclusion process, whereas the Na4EtRA-NaSal system forms an outer-side complex. This fact is also in line with the results obtained from the computational experiments, where the calculated solvation free energy has been found to be more negative for the Na4EtRA-NaSal complex because of the partial entry of the drug inside the Na4EtRA cavity.

Revisiting the Use of Quantum Chemical Calculations in LogPoctanol-water Prediction.

The partition coefficients of drug and drug-like molecules between an aqueous and organic phase are an important property for developing new therapeutics. The predictive power of computational methods is used extensively to predict partition coefficients of molecules. The application of quantum chemical calculations is used to develop methods to develop structure-activity relationship models for such prediction, either based on molecular fragment methods, or via direct calculation of solvation free energy in solvent continuum. The applicability, merits, and shortcomings of these developments are revisited here.