Solvation Free Energy Research Articles

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.

Designing solvent systems using self-evolving solubility databases and graph neural networks.

Designing solvent systems is key to achieving the facile synthesis and separation of desired products from chemical processes, so many machine learning models have been developed to predict solubilities. However, breakthroughs are needed to address deficiencies in the model's predictive accuracy and generalizability; this can be addressed by expanding and integrating experimental and computational solubility databases. To maximize predictive accuracy, these two databases should not be trained separately, and they should not be simply combined without reconciling the discrepancies from different magnitudes of errors and uncertainties. Here, we introduce self-evolving solubility databases and graph neural networks developed through semi-supervised self-training approaches. Solubilities from quantum-mechanical calculations are referred to during semi-supervised learning, but they are not directly added to the experimental database. Dataset augmentation is performed from 11 637 experimental solubilities to >900 000 data points in the integrated database, while correcting for the discrepancies between experiment and computation. Our model was successfully applied to study solvent selection in organic reactions and separation processes. The accuracy (mean absolute error around 0.2 kcal mol-1 for the test set) is quantitatively useful in exploring Linear Free Energy Relationships between reaction rates and solvation free energies for 11 organic reactions. Our model also accurately predicted the partition coefficients of lignin-derived monomers and drug-like molecules. While there is room for expanding solubility predictions to transition states, radicals, charged species, and organometallic complexes, this approach will be attractive to predictive chemistry areas where experimental, computational, and other heterogeneous data should be combined.

Dual Effect of Secondary Solutes on Binding Equilibria: Contributions from Solute-Reactant Interactions and Solute-Water Interactions.

This study examines the role of water in binding equilibria with a special focus on secondary solutes (cosolutes) that influence the equilibrium but are not constituents of the final product. Using a thermodynamic framework that includes an explicit term for the release of water molecules upon binding, this investigation reveals how solutes may alter equilibria by changing the activity of the reactants, reflected in ΔG°(obs), and by changing the chemical potential of the solvent, reflected in ΔGS. The framework is applied to four experimental binding systems that differ in the degree of electrostatic contributions. The model systems include the chelation of Ca2+ by EDTA and three host-guest reactions; the pairings of p-sulfonatocalix[4]arene with tetramethylammonium ion, cucurbit[7]uril with N-acetyl-phenylalanine-amide, and β-cyclodextrin with adamantane carboxylate are tested. Each reaction pair is examined by isothermal titration calorimetry at 25 °C in the presence of a common osmolyte, sucrose, and a common chaotrope, urea. Molar solutions of trehalose and phosphate were also tested with selected models. In general, cosolutes that enhance binding tend to reduce the solvation free energy penalty and cosolutes that weaken binding tend to increase the solvation free energy penalty. Notably, the nonpolar-nonpolar interaction between adamantane carboxylate and β-cyclodextrin is characterized by a ΔGS value near zero. The results with β-cyclodextrin, in particular, prompt further discussions of the hydrophobic effect and the biocompatible properties of trehalose. Other investigators are encouraged to test and refine the approach taken here to further our understanding of solvent effects on molecular recognition.

Dissolution Behavior of Polycyclic Aromatic Hydrocarbons in Heavy Oil in the Presence of Supercritical Cyclohexane.

Supercritical cyclohexane (SC-cyclohexane) shows significant advantages in mild operating conditions and the modulation of product distribution. To gain insights into the upgrading process of heavy oil in SC-cyclohexane, the dissolution process of polycyclic aromatic hydrocarbons (PAHs) contained in heavy oil was simulated based on molecular dynamics with the use of naphthalene, benzopyrene, and mixtures of naphthalene and benzopyrene as the model compounds. As indicated by the radial distribution function results, in SC-cyclohexane exhibiting low density, cyclohexane formed a solvent shell around PAHs such that the local concentration was reduced and the aggregation of PAHs was inhibited. The results of the solvation free energy suggested that van der Waals forces between PAHs and cyclohexane were mainly dominant. As revealed by the dissolution process of the model compounds in SC-cyclohexane, a low density and a suitable temperature contributed to the solubilization of PAHs. An appropriate temperature and a low density can be selected for the upgrading reaction to limit coke formation.

Dissolution behavior, thermodynamic analysis and molecular simulation of lithium bis(fluorosulfonyl)imide in organic solvent at 273.15 K to 313.15 K

The solubility of LiFSI in ethanol, n-propanol, isopropanol, n-butanol, methyl acetate, ethyl acetate, propyl acetate, butyl acetate, dimethyl carbonate, ethyl methyl carbonate, diethyl carbonate at 273.15 K to 313.15 K were determined. Elevated temperatures can actuate the continued mixing of LIFSI in the 11 fluids outlined above. In comparison, the addition of an alkyl group to the carbon chain of a structurally similar solvent molecule will result in a smaller solubility of LiFSI. Overall the molar solubility of LiFSI in esters are significantly higher than the other four alcohols. Besides, the corresponding theoretical results were mounted by fitting the classical equation. The deviation of the fitted values from the measured values showed that the Apelblat equation and Yaws model was more effectively in fitting the solubility of LiFSI. Then, The ΔsolHo, ΔsolGo, ΔsolSo of electrolyte solution were discussed by Van’t Hoff equation. Their fruits implied that the dissolution of LiFSI is found to be accompanied by heat absorption and entropy gain processes. Finally, the solvent molecular polarity index and solvation free energy were calculated by molecular visualization analysis and molecular dynamics simulation, and the correlation analysis with solubility order was carried out to illustrate the role of molecular features on the LiFSI solubility was explained. Electrostatic interactions and molecular polarity are the leading players in determining its solubility limit. These results will provide valuable reference for the purification of LiFSI and the development of new electrolyte solutions.

Modelling of structure and concentration characteristics of water-alcohol solutions

Water-alcohol solutions are widely used in pharmacy, food industry, engineering, etc. The study of their physical and chemical properties has a long history. Nevertheless, due to the complexity of these systems, there is still an interest in their research stimulated by the development of biochemistry in terms of the study and protection of the environment, global climate change, renewable energy resources and cosmochemistry. The purpose of the presented study is the quantum-chemical investigation of water clusters with methyl and ethyl alcohol molecules, as well as the modelling of concentration dependences of the excess molar volume of ethyl alcohol solution in water at different temperatures. Based on quantum-chemical calculations of paired and mixed complexes of water with methanol and ethanol, taking into account solvation effects, study reveals the formation of thermodynamically efficient complexes in liquid, in contrast to the ideal gas state, while in the mixture both individual solvated alcohol molecules and complexes will be in equilibrium. We noted a relative difference in the free energies of solvation of paired and mixed methanol and ethanol complexes. The paper proposes a technique for calculating the excess molar volume in water-alcohol mixtures by approximating the experimental dependences by Lejandre polynomials of the ninth degree. The calculation results showed high accuracy. Hence, continuous functions describe the dependences of the calculated coefficients of Lejandre polynomials on temperature.

Моделирование структуры и концентрационных характеристик водно-спиртовых растворов

Water-alcohol solutions are widely used in pharmacy, food industry, engineering, etc. The study of their physical and chemical properties has a long history. Nevertheless, due to the complexity of these systems, there is still an interest in their research stimulated by the development of biochemistry in terms of the study and protection of the environment, global climate change, renewable energy resources and cosmochemistry. The purpose of the presented study is the quantum-chemical investigation of water clusters with methyl and ethyl alcohol molecules, as well as the modelling of concentration dependences of the excess molar volume of ethyl alcohol solution in water at different temperatures. Based on quantum-chemical calculations of paired and mixed complexes of water with methanol and ethanol, taking into account solvation effects, study reveals the formation of thermodynamically efficient complexes in liquid, in contrast to the ideal gas state, while in the mixture both individual solvated alcohol molecules and complexes will be in equilibrium. We noted a relative difference in the free energies of solvation of paired and mixed methanol and ethanol complexes. The paper proposes a technique for calculating the excess molar volume in water-alcohol mixtures by approximating the experimental dependences by Lejandre polynomials of the ninth degree. The calculation results showed high accuracy. Hence, continuous functions describe the dependences of the calculated coefficients of Lejandre polynomials on temperature.

Coupling finite and boundary element methods to solve the Poisson-Boltzmann equation for electrostatics in molecular solvation.

The Poisson-Boltzmann equation is widely used to model electrostatics in molecular systems. Available software packages solve it using finite difference, finite element, and boundary element methods, where the latter is attractive due to the accurate representation of the molecular surface and partial charges, and exact enforcement of the boundary conditions at infinity. However, the boundary element method is limited to linear equations and piecewise constant variations of the material properties. In this work, we present a scheme that couples finite and boundary elements for the linearised Poisson-Boltzmann equation, where the finite element method is applied in a confined solute region and the boundary element method in the external solvent region. As a proof-of-concept exercise, we use the simplest methods available: Johnson-Nédélec coupling with mass matrix and diagonal preconditioning, implemented using the Bempp-cl and FEniCSx libraries via their Python interfaces. We showcase our implementation by computing the polar component of the solvation free energy of a set of molecules using a constant and a Gaussian-varying permittivity. As validation, we compare against well-established finite difference solvers for an extensive binding energy data set, and with the finite difference code APBS (to 0.5%) for Gaussian permittivities. We also show scaling results from protein G B1 (955 atoms) up to immunoglobulin G (20,148 atoms). For small problems, the coupled method was efficient, outperforming a purely boundary integral approach. For Gaussian-varying permittivities, which are beyond the applicability of boundary elements alone, we were able to run medium to large-sized problems on a single workstation. The development of better preconditioning techniques and the use of distributed memory parallelism for larger systems remains an area for future work. We hope this work will serve as inspiration for future developments that consider space-varying field parameters, and mixed linear-nonlinear schemes for molecular electrostatics with implicit solvent models.

An implicit electrolyte model for plane wave density functional theory exhibiting nonlinear response and a nonlocal cavity definition.

We have developed and implemented an implicit electrolyte model in the Vienna Ab initio Simulation Package (VASP) that includes nonlinear dielectric and ionic responses as well as a nonlocal definition of the cavities defining the spatial regions where these responses can occur. The implementation into the existing VASPsol code is numerically efficient and exhibits robust convergence, requiring computational effort only slightly higher than the original linear polarizable continuum model. The nonlinear + nonlocal model is able to reproduce the characteristic "double hump" shape observed experimentally for the differential capacitance of an electrified metal interface while preventing "leakage" of the electrolyte into regions of space too small to contain a single water molecule or solvated ion. The model also gives a reasonable prediction of molecular solvation free energies as well as the self-ionization free energy of water and the absolute electron chemical potential of the standard hydrogen electrode. All of this, combined with the additional ability to run constant potential density functional theory calculations, should enable the routine computation of activation barriers for electrocatalytic processes.

Soft-sphere continuum solvation models for nonaqueous solvents.

Solvation effects profoundly influence the characteristics and behavior of chemical systems in liquid solutions. The interaction between solute and solvent molecules intricately impacts solubility, reactivity, stability, and various chemical processes. Continuum solvation models gained prominence in quantum chemistry by implicitly capturing these interactions and enabling efficient investigations of diverse chemical systems in solution. In comparison, continuum solvation models in condensed matter simulation are very recent. Among these, the self-consistent continuum solvation (SCCS) and the soft-sphere continuum solvation models (SSCS) have been among the first to be successfully parameterized and extended to model periodic systems in aqueous solutions and electrolytes. As most continuum approaches, these models depend on a number of parameters that are linked to experimental or theoretical properties of the solvent, or that can be tuned based on reference data. Here, we present a systematic parameterization of the SSCS model for over 100 nonaqueous solvents. We validate the model's efficacy across diverse solvent environments by leveraging experimental solvation-free energies and partition coefficients from comprehensive databases. The average root means square error over all the solvents was calculated as 0.85 kcal/mol which is below the chemical accuracy (1 kcal/mol). Similarly to what has been reported by Hille et al. (J. Chem. Phys. 2019, 150, 041710.) for the SCCS model, a single-parameter model accurately reproduces experimental solvation energies, showcasing the transferability and predictive power of these continuum approaches. Our findings underscore the potential for a unified approach to predict solvation properties, paving the way for enhanced computational studies across various chemical environments.

Prediction of Aqueous Reduction Potentials of X•, ChH•, and XO• Radicals with X = Halogen and Ch = Chalcogen.

The aqueous electron affinity and aqueous reduction potentials for F•, Cl•, Br•, I•, OH•, SH•, SeH•, TeH•, ClO•, BrO•, and IO• were calculated using electronic structure methods for explicit cluster models coupled with a self-consistent reaction field (SMD) to treat the aqueous solvent. Calculations were conducted using MP2 and correlated molecular orbital theory up to the CCSD(T)-F12b level for water tetramer clusters and MP2 for octamer cluster. Inclusion of explicit waters was found to be important for accurately predicting the redox potentials in a number of cases. The calculated reduction potentials for X• and ChH• were predicted to within ∼0.1 V of the reported literature values. Fluorine is anomalous due to abstraction of a hydrogen from one of the surrounding water molecules to form a hydroxyl radical and hydrogen fluoride, so its redox potential was calculated using only an implicit model. Larger deviations from experiment were predicted for ClO• and BrO•. These deviations are due to the free energy of solvation of the anion being too negative, as found in the pKa calculations, and that for the neutral being too positive with the current approach.

Interaction of sulfasalazine with outer surface of boron-nitride nanotube as a drug carrier in aqueous solution: insights from quantum mechanics and Monte Carlo simulation

The improvement of the solubility of sulfasalazine in physiological media was the major aim of this study. Accordingly, BNNT inspected as a notable candidate for the carriage of this drug in aqueous media. For this purpose, four possible interactions of two tautomer of sulfasalazine with (9,0) boron-nitride nanotube were considered in aqueous media. The compounds were optimized in gas phase using density functional calculations. Solvation free energies and association free energies of the optimized structures were then studied by Monte Carlo simulation and perturbation method in water environment. Outcomes of quantum mechanical calculations presented that interaction of keto form of sulfasalazine produce the most stable complexes with boron-nitride nanotube in gas phase. Simulation results revealed that electrostatic interactions play a vital role in the intermolecular interaction energies after binding of drug and nanotube in aqueous solution. Results of association free energy calculations indicated that complexes of both two sulfasalazine tautomers (keto and enol) and nanotube were stable in solution. Computed solvation free energies in water showed that the interaction with boron-nitride nanotube significantly improved the solubility of sulfasalazine, which could improve its in vivo bioavailability.

Computational explorations of the interaction between laccase and bisphenol A: influence of surfactant and different organic solvents

ABSTRACT Bisphenol A (BPA), as an environmental endocrine disruptor can cause damage to the reproductive, nervous and immune systems. Laccase can be used to degrade BPA. However, laccase is easily deactivated, especially in organic solvents, but the specific details are not clear. Molecular dynamics simulations were used to investigate the reasons for changes in laccase activity in acetonitrile (ACN) and dimethyl formamide (DMF) solutions. In addition, the effects of ACN and DMF on the activity of laccase and surfactant rhamnolipid (RL) on the degradation of BPA by laccase were investigated. Results showed that addition of ACN changed the structure of the laccase, not only decreasing the van der Waals interaction that promoted the binding of laccase with BPA, but also increasing the polar solvation free energy that hindered the binding of laccase with BPA, so it weakened the laccase activity. DMF greatly enhanced the van der Waals interaction between laccase and BPA, and played a positive role in their binding. The addition of surfactant RL alleviated the effect of organic solvent on the activity of laccase by changing the polar solvation energy. The mechanism of surfactant RL affecting laccase activity in ACN and DMF is described, providing support for understanding the effect of organic solvents on laccase.

GFN-xTB-Based Computations Provide Comprehensive Insights into Emulsion Radiation-Induced Graft Polymerization.

In this article, a deep insight into emulsion radiation-induced graft polymerization (RIGP) was obtained by computing explicit solvation free energies, conformational entropy, monomer radius and dipole moments with the state-of-the-art Conformer-Rotamer Ensemble Sampling Tool (CREST) package primarily at semiempirical GFN-xTB level. By leveraging the robustness of the CREST package, above parameters provided dynamic nature of methacrylate monomers with the consideration of realistic emulsion conditions. With the chemical and physical importance of the above results, CREST-determined explanatory variables sufficiently led to the building of the prediction models for the RIGP of methacrylate monomers. The machine learning model building resulted in effective reactivity predictions and unveiled important factors for the radiation-induced graft polymerization in a chemically interpretable fashion.

Transferable Implicit Solvation via Contrastive Learning of Graph Neural Networks.

Implicit solvent models are essential for molecular dynamics simulations of biomolecules, striking a balance between computational efficiency and biological realism. Efforts are underway to develop accurate and transferable implicit solvent models and coarse-grained (CG) force fields in general, guided by a bottom-up approach that matches the CG energy function with the potential of mean force (PMF) defined by the finer system. However, practical challenges arise due to the lack of analytical expressions for the PMF and algorithmic limitations in parameterizing CG force fields. To address these challenges, a machine learning-based approach is proposed, utilizing graph neural networks (GNNs) to represent the solvation free energy and potential contrasting for parameter optimization. We demonstrate the effectiveness of the approach by deriving a transferable GNN implicit solvent model using 600,000 atomistic configurations of six proteins obtained from explicit solvent simulations. The GNN model provides solvation free energy estimations much more accurately than state-of-the-art implicit solvent models, reproducing configurational distributions of explicit solvent simulations. We also demonstrate the reasonable transferability of the GNN model outside of the training data. Our study offers valuable insights for deriving systematically improvable implicit solvent models and CG force fields from a bottom-up perspective.

Elucidating the role of temperature and water on the π-complexation strength of copper(I) ion-containing ionic liquids using inverse gas chromatography

BackgroundThe π-complexation capability of copper(I) ion has been exploited in olefin/paraffin separations, but its propensity of undergoing disproportionation to copper(II) ion and copper metal has limited its use. Imidazolium-based ionic liquids (ILs) can serve as solvents for copper(I) ions as they facilitate copper(I) ion-olefin complexation and can enhance its stability. To precisely monitor how copper(I) ions complex with olefins in ILs and evaluate the effects of environmental factors, it is necessary to construct an experimental platform capable of quantitatively measuring their molecular-level interactions. ResultsThis study employs an innovative inverse chromatography platform to measure changes in molecular-level interactions between copper(I) ions and olefins when the temperature and water content in the system are carefully controlled. Gas chromatographic stationary phases comprised of the 1-decyl-3-methylimidazolium bis[(trifluoromethyl)sulfonyl]imide ([C10MIM+][NTf2-]) IL containing 0.5 M [Cu+][NTf2-] were pre-heated to 140 °C maximizing copper(I) ion's π-complexation capability. The chromatographic retention of alkenes, dienes, and alkynes on the copper(I) ion/IL stationary phase was observed to be predominantly influenced by their partitioning between the carrier gas and copper(I) ion as well as between the IL and copper(I) ion. Upon introducing water to the system, the Gibbs free energy of solvation for olefins showed less favorable solvation into the stationary phase. In contrast, their solvation was significantly enhanced when the [Cu+][NTf2-]/[C10MIM+][NTf2-] stationary phase was heated to an elevated temperature, indicating that the π-complexation capability of copper(I) ion can be regenerated as needed. SignificanceThis study demonstrates that the stability of copper(I) ions can be improved by dissolving them into appropriate IL solvents. Moreover, the olefin separation performance of the copper(I) ion/IL stationary phase was found to be adjustable by the application of different column treatment conditions (i.e., heating and water introduction), opening the possibility of devising more stable, reliable, and efficient olefin separation systems based on copper(I) ion and IL solvents.

Effect of Main and Side Chains on the Folding Mechanism of the Trp-Cage Miniprotein.

Proteins that do not fold into their functional native state have been linked to diseases. In this study, the influence of the main and side chains of individual amino acids on the folding of the tryptophan cage (Trp-cage), a designed 20-residue miniprotein, was analyzed. For this purpose, we calculated the solvation free energy (SFE) contributions of individual atoms by using the 3D-reference interaction site model with the atomic decomposition method. The mechanism by which the Trp-cage is stabilized during the folding process was examined by calculating the total energy, which is the sum of the conformational energy and SFE. The folding process of the Trp-cage resulted in a stable native state, with a total energy that was 62.4 kcal/mol lower than that of the unfolded state. The solvation entropy, which is considered to be responsible for the hydrophobic effect, contributed 31.3 kcal/mol to structural stabilization. In other words, the contribution of the solvation entropy accounted for approximately half of the total contribution to Trp-cage folding. The hydrophobic core centered on Trp6 contributed 15.6 kcal/mol to the total energy, whereas the solvation entropy contribution was 6.3 kcal/mol. The salt bridge formed by the hydrophilic side chains of Asp9 and Arg16 contributed 10.9 and 5.0 kcal/mol, respectively. This indicates that not only the hydrophobic core but also the salt bridge of the hydrophilic side chains gain solvation entropy and contribute to stabilizing the native structure of the Trp-cage.

Molecular dynamics simulations of the solubility and conformation change of chitosan grafted polyacrylamide: Impact of grafting rate

Graft modification is an effective approach to improve the water solubility and molecular weight of chitosan. This work investigated the effect of grafting rate on water solubility of chitosan-grafted polyacrylamide (Chi-g-PAM) using molecular dynamics simulations. The intramolecular and intermolecular interaction between Chi-g-PAM with the grafting rate of 100%–250% and water together with the conformation change were analyzed by radial distribution function (RDF), radius of gyration, solvation free energy (ΔGSFE), mean square displacement, diffusion coefficient and shear viscosity. The results showed that the intramolecular hydrogen bonding of Chi-g-PAM was the dominant factor affecting its water solubility. While the grafting rate of Chi-g-PAM increased from 100% to 250%, the RDF of Chi-g-PAM decreased from 5.39 to 3.57 between O–H at 2.0 Å and 2.60 to 1.30 between N–H at 2.1 Å. The interaction between Chi-g-PAM and water increased with grafting rate as the absolute value of ΔGSFE for Chi-g-PAM increased from 432.917 kcal/mol to 858.749 kcal/mol. The solubility of Chi-g-PAM in acidic solution was much better than that of Chi-g-PAM in aqueous solution. Our work on the fundamental insights into the effect of grafted molecular structure of chitosan-based copolymer on its solubility will benefit the development of environmentally friendly chitosan-based flocculants.

Investigation of the structure, stability, and relative solubility of psilocybin in water and pure organic solvents: A molecular simulation study

Psilocybin is an indole-based secondary metabolite found naturally in mushrooms which possesses several pharmacological effects. Recently, a large number of experimental investigations have been conducted to characterize the pharmacology of psilocybin and its derivatives, and to develop synthetic pathways to manufacture psilocybin. Nonetheless, current research on the physical characterization of psilocybin is limited in part due to legal restrictions. In the present study, we investigate two unique tautomers of psilocybin as depicted in the 2D chemical structure of psilocybin presented in the recent literature. Using a combination of electronic structure calculations and molecular simulation, we are able to identify and characterize the thermodynamically preferred tautomer. We additionally computed the solvation free energy and investigated the solvation structure of psilocybin in water and 35 organic solvents. We find that hydrogen bonding between psilocybin and the solvent dominates the solvation process. Considering the thermodynamically preferred tautomer, we find that the solubility in water is greater than all of the studied organic solvents.

Measurement of the solubility and density of the saturated solution of 3-aminophenol in different pure solvents from 283.1 to 323.1 K: Correlative to pure predictive thermodynamic modeling and molecular dynamic simulation

The solubility of 3-aminophenol was measured in water, isopropanol, ethyl acetate, chloroform, and tert-butyl methyl ether (MTBE) from 283.1 to 323.1 K at atmospheric pressure. In addition, the density of pure solvents and the saturated 3-aminophenol solutions was measured. The experimental solubility data were correlated and predicted using four types of thermodynamic models, including correlative (Wilson, NRTL, and UNIQUAC), semi-predictive (NRTL-SAC, and UNIQAUC-SAC), predictive group contribution methods (UNIFAC, and UNIFAC-DMD), and pure predictive COSMO-SAC model. The NRTL-SAC and UNIQUAC-SAC as semi-predictive models were used to obtain 3-aminophenol conceptual segment numbers, and then, using the NRTL-SAC model, the solubility of 3-aminophenol was predicted in 58 FDA approved solvents in the pharmaceutical industry. The predictive models including UNIFAC and UNIFAC-DMD were also used to predict the 3-aminophenol solubility and compared with the experimental data. The results of the COSMO-SAC model have a large deviation from experimental data. In addition, the experimental solubility data were correlated using the PR equation of state with relative success. The segment-based models such as NRTL-SAC and UNIQUAC-SAC are suggested in the fast solvent screening procedure. Finally, the standard dissolution enthalpy (ΔdisH), the standard dissolution entropy (ΔdisS), and the standard dissolution Gibbs energy (ΔdisG) of 3-aminophenol in the selected solvents were calculated by the Van’t Hoff model and the Gibbs-Helmholtz equation. These values imply that the solubility of 3-aminophenol is an entropy-driven non-spontaneous endothermic process in all chosen single solvents. Using the molecular dynamic (MD) simulation the solvation free energy of 3-aminophenol was calculated. The order of the absolute value of free energy of solvation is as ethyl acetate > isopropanol > MTBE > water > chloroform which is consistent with the order of solubility data.