1-Octanol/Water Partition Coefficients of n-Alkanes from Molecular Simulations of Absolute Solvation Free Energies

Molecular insights into the interactions of theaflavin and epicatechin with different lipid bilayer membranes by molecular dynamics simulation

At present, consumers increasingly favored the natural food preservatives with fewer side-effects on health. The green tea catechins and black tea theaflavins attracted considerable interest, and their antibacterial effects were extensively reported in the literature. Epicatechin (EC), a green tea catechin without a gallate moiety, showed no bactericidal activity, whereas the theaflavin (TF), also lacking a gallate moiety, exhibited potent bactericidal activity, and the antibacterial effects of green tea catechins and black tea theaflavins were closely correlated with their abilities to disrupt the bacterial cell membrane. In our present study, the mechanisms of membrane interaction modes and behaviors of TF and EC were explored by molecular dynamics simulations. It was demonstrated that TF exhibited markedly stronger affinity for the POPG bilayer compared to EC. Additionally, the hydrophobic interactions of tropolone/catechol rings with the acyl chain part could significantly contribute to the penetration of TF into the POPG bilayer. It was also found that the resorcinol/pyran rings were the key functional groups in TF for forming hydrogen bonds with the POPG bilayer. We believed that the findings from our current study could offer useful insights to better understand the stronger antibacterial effects of TF compared to EC.

Investigation of cathinone analogs targeting human dopamine transporter using molecular modeling

In a step towards understanding the structure-property relationship among Synthetic Cathinones (SCs), a combined methodology based on Density Functional Theory (DFT), Administration, Distribution, Metabolism, Excretion, and Toxicity (ADMET) predictions, docking and molecular dynamics simulations have been applied to correlate physicochemical descriptors of various SCs to their biological activity. The results from DFT and molecular docking studies correlate well with each other explaining the biological activity trends of the studied SCs. Quantum mechanical descriptors viz. polarizability, electron affinity, ionization potential, chemical hardness, electronegativity, molecular electrostatic potential, and ion interaction studies unravel the distinguishingly reactive nature of Group D (pyrrolidine substituted) and Group E (methylenedioxy and pyrrolidine substituted) compounds. According to ADMET analysis, Group D and Group E molecules have a higher probability of permeating through the blood-brain barrier. Molecular docking results indicate that Phe76, Ala77, Asp79, Val152, Tyr156, Phe320, and Phe326 constitute the binding pocket residues of hDAT in which the most active ligands MDPV, MDPBP, and MDPPP are bound. Finally, to validate the derived quantum chemical descriptors and docking results, Molecular Dynamics (MD) simulations are performed with homology-modelled hDAT (human dopamine transporter). The MD simulation results revealed that the majority of SCs remain stable within the hDAT protein's active sites via non-bonded interactions after 100 ns long simulations. The findings from DFT, ADMET analysis, molecular docking, and molecular dynamics simulation studies complement each other suggesting that pyrrolidine-substituted SCs (Group D and E), specifically, MPBP and PVN are proven potent SCs along with MDPV, validating various experimental observations.Communicated by Ramaswamy H. Sarma.

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.

The reconciliation between the experimental and calculated octanol-water partition coefficient of 1,2-dipalmitoyl-sn-glycero-3-phosphatidylcholine using atomistic molecular dynamics: an open question

The octanol-water partition coefficient of 1,2-dipalmitoyl-sn-glycero-3-phosphatidylcholine (DPPC) was investigated using atomistic molecular dynamics simulations via thermodynamic integration and multistate Bennett acceptance ratio methods. The GAFF and CHARMM36 force fields were used with six water models widely used in molecular dynamics simulations. The OPC4 water model provided the best agreement with the experimental octanol-water partition coefficient of DPPC using the two force fields. However, there is still plenty of room for improvement in water models with correct estimation of surface tension that uses better and suitable non-bonded interaction parameters between water-water and water-DPPC. The Gibbs free energy of transferring DPPC from octanol to water phase was calculated to be 19.8 ± 0.3 and 20.2 ± 0.3 kcal mol-1, giving a partition coefficient of 14.5 ± 0.4 and 14.8 ± 0.3 for the GAFF and CHARMM36 force fields, respectively. This study reinforces the importance of developing new water models that reproduce experimental surface tensions to reconcile the water-water and water-DPPC non-bonded interactions and the existing discrepancy between experimental measurements of amphiphilic molecules that are important in many areas of scientific applications and industry such as biophysics, surfactant, colloids, membranes, medicine, nanotechnology, and food and pharmaceutical industries, and so on. It raises two important open questions: Is the experimental octanol-water partition coefficient of DPPC reliable? Or is its calculation accurate using the OPC4 water model? With respect to the experimental measurements, there may be non-treated aspects such as the formation of aggregates in aqueous phase and limit of detection of the applied method. And, in the calculation, some effects are not possible to be considered in a correct way or viable time such as calculating quantum effects, sampling all conformations, considering phase transitions, and correctly evaluating the intermolecular forces to estimate an accurate surface tension.Communicated by Ramaswamy H. Sarma.

A comparative study of 5- fluorouracil, doxorubicin, methotrexate, paclitaxel for their inhibition ability for Mpro of nCoV: Molecular docking and molecular dynamics simulations

A new corona virus (nCoV) is aetiological agent responsible for the viral pneumonia epidemic. Three is no specific therapeutic medicines available for the treatment of this condition and also effective treatment choices are few. In this work author tried to investigate some repurposing drug such as 5- fluorouracil, doxorubicin, methotrexate and paclitaxel against the main protease (Mpro) of nCoV by the computational model. Molecular docking was performed to screen out the best compound and doxorubicin was found to have minimum binding energy −121.89 kcal/mol. To further study, MD simulations were performed at 300 K and the result successfully corroborate the energy obtained by molecular docking. Temperature dependent MD simulation of the best molecule that is doxorubicin obtained from docking result was performed to check the variation in structural changes in Mpro of nCoV at 290 K, 310 K, 320 K and 325 K. It is sound that doxorubicin binds effectively with Mpro of nCoV at 290 K. Further ADME properties of the 5- fluorouracil, doxorubicin, methotrexate and paclitaxel were also evaluated to understand the bioavailability.

A New Formulation for the Concerted Alchemical Calculation of van der Waals and Coulomb Components of Solvation Free Energies

Alchemical free energy calculations via molecular dynamics have been widely used to obtain thermodynamic properties related to protein-ligand binding and solute-solvent interactions. Although soft-core modeling is the most common approach, the linear basis function (LBF) methodology [Naden, L. N.; et al. J. Chem. Theory Comput.2014, 10 (3), 1128; 2015, 11 (6), 2536] has emerged as a suitable alternative. It overcomes the end-point singularity of the scaling method while maintaining essential advantages such as ease of implementation and high flexibility for postprocessing analysis. In the present work, we propose a simple LBF variant and formulate an efficient protocol for evaluating van der Waals and Coulomb components of an alchemical transformation in tandem, in contrast to the prevalent sequential evaluation mode. To validate our proposal, which results from a careful optimization study, we performed solvation free energy calculations and obtained octanol-water partition coefficients of small organic molecules. Comparisons with results obtained via the sequential mode using either another LBF approach or the soft-core model attest to the effectiveness and correctness of our method. In addition, we show that a reaction field model with an infinite dielectric constant can provide very accurate hydration free energies when used instead of a lattice-sum method to model solute-solvent electrostatics.

Temperature induced change of TMAO effects on hydrophobic hydration

The effect of trimethylamine-N-oxide (TMAO) on hydrophobic solvation and hydrophobic interactions of methane has been studied with Molecular Dynamics simulations in the temperature range between 280 and 370 K at 1 bar ambient pressure. We observe a temperature transition in the effect of TMAO on the aqueous solubility of methane. At low temperature (280 K), methane is preferentially hydrated, causing TMAO to reduce its solubility in water, while above 320 K, methane preferentially interacts with TMAO, causing TMAO to promote its solubility in water. Based on a statistical-mechanical analysis of the excess chemical potential of methane, we find that the reversible work of creating a repulsive methane cavity opposes the solubility of methane in TMAO/water solution more than in pure water. Below 320 K, this solvent-excluded volume effect overcompensates the contribution of methane–TMAO van der Waals interactions, which promote the solvation of methane and are observed at all temperatures. These van der Waals interactions with the methyl groups of TMAO tip the balance above 320 K where the effect of TMAO on solvent-excluded volume is smaller. We furthermore find that the effective attraction between dissolved methane solutes increases with the increasing TMAO concentration. This observation correlates with a reduction in the methane solubility below 320 K but with an increase in methane solubility at higher temperatures.

Comparison of logP and logD correction models trained with public and proprietary data sets

In drug discovery, partition and distribution coefficients, logP and logD for octanol/water, are widely used as metrics of the lipophilicity of molecules, which in turn have a strong influence on the bioactivity and bioavailability of potential drugs. There are a variety of established methods, mostly fragment or atom-based, to calculate logP while logD prediction generally relies on calculated logP and pKa for the estimation of neutral and ionized populations at a given pH. Algorithms such as ClogP have limitations generally leading to systematic errors for chemically related molecules while pKa estimation is generally more difficult due to the interplay of electronic, inductive and conjugation effects for ionizable moieties. We propose an integrated machine learning QSAR modeling approach to predict logD by training the model with experimental data while using ClogP and pKa predicted by commercial software as model descriptors. By optimizing the loss function for the ClogD calculated by the software, we build a correction model that incorporates both descriptors from the software and available experimental logD data. Additionally, we calculate logP from the logD model using the software predicted pKa's. Here, we have trained models using publicly or commercial available logD data to show that this approach can improve on commercial software predictions of lipophilicity. When applied to other logD data sets, this approach extends the domain of applicability of logD and logP predictions over commercial software. Performance of these models favorably compare with models built with a larger set of proprietary logD data.

Simulating drug concentrations in PDMS microfluidic organ chips

Microfluidic organ-on-a-chip (Organ Chip) cell culture devices are often fabricated using polydimethylsiloxane (PDMS) because it is biocompatible, transparent, elastomeric, and oxygen permeable; however, hydrophobic small molecules can absorb to PDMS, which makes it challenging to predict drug responses. Here, we describe a combined simulation and experimental approach to predict the spatial and temporal concentration profile of a drug under continuous dosing in a PDMS Organ Chip containing two parallel channels separated by a porous membrane that is lined with cultured cells, without prior knowledge of its log P value. First, a three-dimensional finite element model of drug loss into the chip was developed that incorporates absorption, adsorption, convection, and diffusion, which simulates changes in drug levels over time and space as a function of potential PDMS diffusion coefficients and log P values. By then experimentally measuring the diffusivity of the compound in PDMS and determining its partition coefficient through mass spectrometric analysis of the drug concentration in the channel outflow, it is possible to estimate the effective log P range of the compound. The diffusion and partition coefficients were experimentally derived for the antimalarial drug and potential SARS-CoV-2 therapeutic, amodiaquine, and incorporated into the model to quantitatively estimate the drug-specific concentration profile over time measured in human lung airway chips lined with bronchial epithelium interfaced with pulmonary microvascular endothelium. The same strategy can be applied to any device geometry, surface treatment, or in vitro microfluidic model to simulate the spatial and temporal gradient of a drug in 3D without prior knowledge of the partition coefficient or the rate of diffusion in PDMS. Thus, this approach may expand the use of PDMS Organ Chip devices for various forms of drug testing.

Predicting octanol/water partition coefficients using molecular simulation for the SAMPL7 challenge: comparing the use of neat and water saturated 1-octanol

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.

Performance of molecular dynamics simulation for predicting of solvation free energy of neutral solutes in methanol

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 (R 2) for molecular dynamics simulation was 0.9618, which shows higher accuracy of MD simulation for calculating solvation free energy comparing to two other models.

Solvent similarity index

The Solvent Similarity Index (SSI) is a quantitative parameter we introduce for the comparison of the solvation properties of any solvent or solvent mixture. The Surface Site Interaction Model for Liquids at Equilibrium (SSIMPLE) was used to calculate the free energy of solvation of a single Surface Site Interaction Point (SSIP) on a solute. The SSIP representation of molecular surfaces was used to calculate the free energy of solvation for all possible solute polarities, generating a unique solvation profile for any solvent or solvent mixture. Quantitative comparison of the solvation profiles of two solvents was used as the basis for calculating the solvation similarity index. Values of SSI were calculated for all pairwise comparisons of 261 pure solvents at 298 K, and the results were used to classify solvents into groups according to their solvation properties. Applications to understanding the solvation properties of binary solvent mixtures and for identification of alternative solvents are illustrated.

Predicting octanol/water partition coefficients for the SAMPL6 challenge using the SM12, SM8, and SMD solvation models

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.

Predicting Octanol–Water Partition Coefficients of Fluorinated Drug-Like Molecules: A Combined Experimental and Theoretical Study

In this paper, a dataset of 11 fluorinated compounds containing a variety of functional groups (amides, esters, indoles, and ethers) as well as mono, gem-difluoro, erythro-difluoro, and threo-difluoro patterns were synthesised and their octanol–water partition coefficients (log P) were measured using a shake-flask method. The resulting data was used to assess the performance of several popular empirical fragment-based methods as well as quantum chemical implicit solvent models (SMD and SM12). Overall, the empirical miLOGP, ALOGPS, and ALOGP methods performed the best with a mean absolute deviation (MAD) of ~0.25 log units, while the best performing implicit solvent model SMD has a slightly higher MAD of 0.36 log units. Based on the present work and previous studies, the miLOGP and ALOGP empirical methods are recommended for fast and moderately accurate prediction of log P for neutral organic solutes.

Predicting the Time of Entry of Nanoparticles in Lipid Membranes

The number of engineered nanoparticles for applications in the biomedical arena has grown tremendously over the last years due to advances in the science of synthesis and characterization. For most applications, the crucial step is the transport through a physiological cellular membrane. However, the behavior of nanoparticles in a biological matrix is a very complex problem that depends not only on the type of nanoparticle but also on its size, shape, phase, surface charge, chemical composition, and agglomeration state. In this paper, we introduce a streamlined theoretical model that predicts the average time of entry of nanoparticles in lipid membranes, using a combination of molecular dynamics simulations and statistical approaches. The model identifies four parameters that separate the contributions of nanoparticle characteristics (i.e., size, shape, solubility) from the membrane properties (density distribution). This factorization allows the inclusion of data obtained from both experimental and computational sources, as well as a rapid estimation of large sets of permutations in membranes. The robustness of the model is supported by experimental data carried out in lipid vesicles encapsulating graphene quantum dots as nanoparticles. Given the high level of interest across multiple areas of study in modulating intracellular targets, and the need to understand and improve the applications of nanoparticles and to assess their effect on human health (i.e., cytotoxicity, bioavailability), this work contributes to the understanding and prediction of interactions between nanoparticles and lipid membranes.

Predicting Octanol-Water Partition Coefficients: Are Quantum Mechanical Implicit Solvent Models Better than Empirical Fragment-Based Methods?

In this work, we examined the performance of contemporary quantum mechanical implicit solvent models (SMD, SM8, SM12, and ADF-COSMO-RS) and empirical fragment-based methods for predicting octanol-water partition coefficients (log P_ow). Two test sets were chosen: the first is composed of 34 organic molecules from a recent study by Mobley J. Chem. Theory Comput , 2016 , 12 , 4015 - 4024 , and the second set is based on a collection of 55 fluorinated alkanols and carbohydrates from Linclau Angew. Chem., Int. Ed. , 2016 , 55 , 674 - 678 . Our analysis indicates that the errors in the solvation free energies of implicit models are reasonably systematic in both solvents such that there is substantial cancellation of errors in the calculation of transfer free energies. Overall, implicit solvent models performed very well across the two test sets with mean absolute errors (MAEs) of about 0.6 log unit and are superior to explicit solvent simulations (GAFF and GAFF-DC). Interestingly, the best performers were empirical fragment-based methods, including ALOGP and miLOGP with significantly lower MAEs (0.2 to 0.4 log unit). The ALOGP method was further tested against the recent SAMPL6 log P_ow challenge consisting of 11 drug-like molecules where it obtained an MAE of 0.32 log unit compared to the best-performing COSMOtherm model (0.31 log unit).

Molecular simulation as a computational pharmaceutics tool to predict drug solubility, solubilization processes and partitioning

In this review we will discuss how computational methods, and in particular classical molecular dynamics simulations, can be used to calculate solubility of pharmaceutically relevant molecules and systems. To the extent possible, we focus on the non-technical details of these calculations, and try to show also the added value of a more thorough and detailed understanding of the solubilization process obtained by using computational simulations. Although the main focus is on classical molecular dynamics simulations, we also provide the reader with some insights into other computational techniques, such as the COSMO-method, and also discuss Flory-Huggins theory and solubility parameters. We hope that this review will serve as a valuable starting point for any pharmaceutical researcher, who has not yet fully explored the possibilities offered by computational approaches to solubility calculations.

Uncovering abnormal changes in logP after fluorination using molecular dynamics simulations

The fluorination-induced changes in the logP (1-octanol/water partition coefficient) of ligands were examined by molecular dynamics simulations. The protocol and force field parameters were first evaluated by calculating the logP values for n-alkanes, and their monofluorinated and monochlorinated analogs. Then, the logP values of several test sets (1-butanol, 3-propyl-1H-indole, and analogs fluorinated at the terminal methyl group) were calculated. The calculated results agree well with experiment, and the root mean square error values are 0.61 and 0.68 log units for the GAFF and GAFF2 force fields, respectively. Finally, the logP estimation was extended to a drug molecule, TAK-438, for which fluorination-induced abnormal logP reduction has been observed experimentally. This abnormal change was qualitatively reproduced by the molecular dynamics simulations. We found that the abnormal logP reduction can be mainly attributed to the effect of fluorination-induced dipole change. Our results suggest that molecular simulation is a useful strategy to predict the fluorination-induced change in logP for drug discovery applications.

Dermal Delivery of Selected Polyphenols from Silybum marianum. Theoretical and Experimental Study

Silymarin is a well-known standardized extract from the seeds of milk thistle (Silybum marianum L., Asteraceae) with a pleiotropic effect on human health, including skin anticancer potential. Detailed characterization of flavonolignans properties affecting interactions with human skin was of interest. The partition coefficients log Pow of main constitutive flavonolignans, taxifolin and their respective dehydro derivatives were determined by a High Performance Liquid Chromatography (HPLC) method and by mathematical (in silico) approaches in n-octanol/water and model lipid membranes. These parameters were compared with human skin intake ex vivo. The experimental log Pow values for individual diastereomers were estimated for the first time. The replacement of n-octanol with model lipid membranes in the theoretical lipophilicity estimation improved the prediction strength. During transdermal transport, all the studied compounds permeated the human skin ex vivo; none of them reached the acceptor liquid. Both experimental/theoretical tools allowed the studied polyphenols to be divided into two groups: low (taxifolin, silychristin, silydianin) vs. high (silybin, dehydrosilybin, isosilybin) lipophilicity and skin intake. In silico predictions can be usefully applied for estimating general lipophilicity trends, such as skin penetration or accumulation predictions. However, the theoretical models cannot yet provide the dermal delivery differences of compounds with very similar physico-chemical properties; e.g., between diastereomers.

Calculation of the water-octanol partition coefficient of cholesterol for SPC, TIP3P, and TIP4P water

We present a numerical study of the relative solubility of cholesterol in octanol and water. Our calculations allow us to compare the accuracy of the computed values of the excess chemical potential of cholesterol for several widely used water models (SPC, TIP3P, and TIP4P). We compute the excess solvation free energies by means of a cavity-based method [L. Li et al., J. Chem. Phys. 146(21), 214110 (2017)] which allows for the calculation of the excess chemical potential of a large molecule in a dense solvent phase. For the calculation of the relative solubility ("partition coefficient," log₁₀ P o / w ) of cholesterol between octanol and water, we use the OPLS/AA force field in combination with the SPC, TIP3P, and TIP4P water models. For all water models studied, our results reproduce the experimental observation that cholesterol is less soluble in water than in octanol. While the experimental value for the partition coefficient is log₁₀ P o / w = 3.7, SPC, TIP3P, and TIP4P give us a value of log₁₀ P o / w = 4.5, 4.6, and 2.9, respectively. Therefore, although the results for the studied water models in combination with the OPLS/AA force field are acceptable, further work to improve the accuracy of current force fields is needed.

1-Butanol as a Solvent for Efficient Extraction of Polar Compounds from Aqueous Medium: Theoretical and Practical Aspects

The extraction of polar molecules from aqueous solution is a challenging task in organic synthesis. 1-Butanol has been used sporadically as an eluent for polar molecules, but it is unclear which molecular features drive its efficiency. Here, we employ free energy simulations to study the partitioning of 15 solutes between water and 1-butanol. The simulations demonstrate that the high affinity of polar molecules to the wet 1-butanol phase is associated with its nanostructure. Small inverse micelles of water are able to accommodate polar solutes and locally mimic an aqueous environment. We verify the simulations based on partition coefficients between water and 1-octanol, and include a blind prediction of the water/1-butanol partition coefficient of cyclohexane-1,2-diol. The calculations are in excellent agreement with experiment, reaching root-mean-square deviations below 0.7 kcal/mol. Actual extractions of cyclohexane-1,2-diol from buffer solutions that mimic cell lysates and suspensions in biocatalytic reactions further exemplify our findings. The yields highlight that extractions with 1-butanol can be significantly more efficient than the conventional protocol based on ethyl acetate.

Partitioning of Cs+ and Na+ ions by dibenzo-18-crown-6 ionophore in biphasic aqueous systems of octanol and ionic liquid

Molecular dynamics (MD) simulations were carried out to obtain molecular level insights on the behavior of Cs+/Na+ ions at the water–ionic liquid and water–octanol interface in the presence of dibenzo-18-crown-6 (DB18C6) ionophore with an aim to compare an ionic liquid (IL) to a octanol as receiving organic solvent phase. It was observed that the rate of phase separation for the octanol system was rapid as compared to the IL system. The free crown ethers (CE) were found to be highly solvated by the IL phase. A dual cationic exchange mechanism was observed at the [BMIM]/water interface. The [BMIM]+ cation was found to exchange with both the metal ions in aqueous phase as well as with the metal ion aided by the ionophore. The self-diffusion coefficient of the 1:2 complex (0.07×10−9 m2/s) at the octanol/water interface were found to be smaller than that of 1:1 complex (0.37 and 0.14×10−9 m2/s). It was observed that the surface tension of ILs decreased in the presence of complexes and free CE, whereas the surface tension of water was found to increase in presence of salts (Cs+NO3 − and Na+NO3 −). The experimentally determined value of D Cs was found to be quite high in IL phase (1.595) compared to the octanol phase (0.139) in presence of CE. The kinetics of Cs+ was found to be very fast having rate with values of k 1 ^ $\widehat {{k_1}}$ =1.79×10−12 s−1 and k 2 ^ $\widehat {{k_2}}$ =0.205×10−12 s−1 in IL and water phase, respectively. The present results may help us in understanding the role of diluents in the assisted metal ion extraction but also in the future design of diluents and ionophore.

Extraction of Gd3+ and UO22+ Ions Using Polystyrene Grafted Dibenzo Crown Ether (DB18C6) with Octanol and Nitrobenzene: A Molecular Dynamics Study

Atomistic molecular dynamics (MD) simulations are performed in order to derive thermodynamic properties important to understand the extraction of gadolinium (Gd³⁺) and uranium dioxide (UO₂) with dibenzo crown ether (DBCE) in nitrobenzene (NB) and octanol (OCT) solvents. The effect of polystyrene graft length, on DBCE, on the binding behavior of Gd³⁺ and UO₂²⁺ is investigated for the first time. Our simulation results demonstrate that the binding of Gd³⁺ and UO₂²⁺ onto the oxygens of crown ethers is favorable for polystyrene grafted crown ether in the organic solvents OCT and NB. The metal ion binding free energy (ΔG_Binding) in different solvent environments is calculated using the thermodynamic integration (TI) method. ΔG_Binding becomes more favorable in both solvents, NB and OCT, with an increase in the polystyrene monomer length. The metal ion transferability from an aqueous phase to an organic phase is estimated by calculating transfer free-energy calculations (ΔG_Transfer). ΔG_Transfer is significantly favorable for both Gd³⁺ and UO₂²⁺ for the transfer from the aqueous phase to the organic phase (i.e., NB and OCT) via ion-complexation to DBCE with an increase in polystyrene length. The partition coefficient (log P) values for Gd³⁺ and UO₂²⁺ show a 5-fold increase in separation capacity with polystyrene grafted DBCE. We corroborate the observed behavior by further analyzing the structural and dynamical properties of the ions in different phases.

Tinker-OpenMM: Absolute and relative alchemical free energies using AMOEBA on GPUs

The capabilities of the polarizable force fields for alchemical free energy calculations have been limited by the high computational cost and complexity of the underlying potential energy functions. In this work, we present a GPU-based general alchemical free energy simulation platform for polarizable potential AMOEBA. Tinker-OpenMM, the OpenMM implementation of the AMOEBA simulation engine has been modified to enable both absolute and relative alchemical simulations on GPUs, which leads to a ∼200-fold improvement in simulation speed over a single CPU core. We show that free energy values calculated using this platform agree with the results of Tinker simulations for the hydration of organic compounds and binding of host-guest systems within the statistical errors. In addition to absolute binding, we designed a relative alchemical approach for computing relative binding affinities of ligands to the same host, where a special path was applied to avoid numerical instability due to polarization between the different ligands that bind to the same site. This scheme is general and does not require ligands to have similar scaffolds. We show that relative hydration and binding free energy calculated using this approach match those computed from the absolute free energy approach. © 2017 Wiley Periodicals, Inc.

Approaches for calculating solvation free energies and enthalpies demonstrated with an update of the FreeSolv database

Solvation free energies can now be calculated precisely from molecular simulations, providing a valuable test of the energy functions underlying these simulations. Here, we briefly review "alchemical" approaches for calculating the solvation free energies of small, neutral organic molecules from molecular simulations, and illustrate by applying them to calculate aqueous solvation free energies (hydration free energies). These approaches use a non-physical pathway to compute free energy differences from a simulation or set of simulations and appear to be a particularly robust and general-purpose approach for this task. We also present an update (version 0.5) to our FreeSolv database of experimental and calculated hydration free energies of neutral compounds and provide input files in formats for several simulation packages. This revision to FreeSolv provides calculated values generated with a single protocol and software version, rather than the heterogeneous protocols used in the prior version of the database. We also further update the database to provide calculated enthalpies and entropies of hydration and some experimental enthalpies and entropies, as well as electrostatic and nonpolar components of solvation free energies.

Predicting hydrophobic solvation by molecular simulation: 1. Testing united-atom alkane models

We present a systematic test of the performance of three popular united-atom force fields-OPLS-UA, GROMOS and TraPPE-at predicting hydrophobic solvation, more precisely at describing the solvation of alkanes in alkanes. Gibbs free energies of solvation were calculated for 52 solute/solvent pairs from Molecular Dynamics simulations and thermodynamic integration making use of the IBERCIVIS volunteer computing platform. Our results show that all force fields yield good predictions when both solute and solvent are small linear or branched alkanes (up to pentane). However, as the size of the alkanes increases, all models tend to increasingly deviate from experimental data in a systematic fashion. Furthermore, our results confirm that specific interaction parameters for cyclic alkanes in the united-atom representation are required to account for the additional excluded volume within the ring. Overall, the TraPPE model performs best for all alkanes, but systematically underpredicts the magnitude of solvation free energies by about 6% (RMSD of 1.2 kJ/mol). Conversely, both GROMOS and OPLS-UA systematically overpredict solvation free energies (by ∼13% and 15%, respectively). The systematic trends suggest that all models can be improved by a slight adjustment of their Lennard-Jones parameters. © 2016 Wiley Periodicals, Inc.

Blind prediction of cyclohexane-water distribution coefficients from the SAMPL5 challenge

In the recent SAMPL5 challenge, participants submitted predictions for cyclohexane/water distribution coefficients for a set of 53 small molecules. Distribution coefficients (log D) replace the hydration free energies that were a central part of the past five SAMPL challenges. A wide variety of computational methods were represented by the 76 submissions from 18 participating groups. Here, we analyze submissions by a variety of error metrics and provide details for a number of reference calculations we performed. As in the SAMPL4 challenge, we assessed the ability of participants to evaluate not just their statistical uncertainty, but their model uncertainty-how well they can predict the magnitude of their model or force field error for specific predictions. Unfortunately, this remains an area where prediction and analysis need improvement. In SAMPL4 the top performing submissions achieved a root-mean-squared error (RMSE) around 1.5 kcal/mol. If we anticipate accuracy in log D predictions to be similar to the hydration free energy predictions in SAMPL4, the expected error here would be around 1.54 log units. Only a few submissions had an RMSE below 2.5 log units in their predicted log D values. However, distribution coefficients introduced complexities not present in past SAMPL challenges, including tautomer enumeration, that are likely to be important in predicting biomolecular properties of interest to drug discovery, therefore some decrease in accuracy would be expected. Overall, the SAMPL5 distribution coefficient challenge provided great insight into the importance of modeling a variety of physical effects. We believe these types of measurements will be a promising source of data for future blind challenges, especially in view of the relatively straightforward nature of the experiments and the level of insight provided.

Calculating Partition Coefficients of Small Molecules in Octanol/Water and Cyclohexane/Water

Partition coefficients describe how a solute is distributed between two immiscible solvents. They are used in drug design as a measure of a solute's hydrophobicity and a proxy for its membrane permeability. We calculate partition coefficients from transfer free energies using molecular dynamics simulations in explicit solvent. Setup is done by our new Solvation Toolkit which automates the process of creating input files for any combination of solutes and solvents for many popular molecular dynamics software packages. We calculate partition coefficients between octanol/water and cyclohexane/water with the Generalized AMBER Force Field (GAFF) and the Dielectric Corrected GAFF (GAFF-DC). With similar methods in the past we found a root-mean-squared error (RMSE) of 6.3 kJ/mol in hydration free energies which would correspond to an error of around 1.6 log units in partition coefficients if solvation free energies in both solvents were estimated with comparable accuracy. Here we find an overall RMSE of about 1.2 log units with both force fields. Results from GAFF and GAFF-DC seem to exhibit systematic biases in opposite directions for calculated cyclohexane/water partition coefficients.

Predictions of the physicochemical properties of amino acid side chain analogs using molecular simulation

A candidate drug compound is released for clinical trails (in vivo activity) only if its physicochemical properties meet desirable bioavailability and partitioning criteria.

The Use of Anisotropic Potentials in Modeling Water and Free Energies of Hydration

We propose a novel, anisotropic rigid-body intermolecular potential model to predict the properties of water and the hydration free energies of neutral organic solutes. The electrostatic interactions of water and the solutes are modeled using atomic multipole moments up to hexadecapole; these are obtained from distributed multipole analysis of the quantum mechanically computed charge densities and include average polarization effects in solution. The repulsion-dispersion water-water interactions are modeled with a three-site, exp-6 model fitted to the experimental liquid water density and oxygen-oxygen radial distribution function at ambient conditions. The proposed water model reproduces well several water properties not used in its parametrization, including vapor-liquid coexistence densities, the maximum in liquid water density at atmospheric pressure, the structure of ordered ice polymorphs, and the liquid water heat capacity. The model is used to compute the hydration free energy of 10 neutral organic solutes using explicit-solvent free energy perturbation. The solute-solute repulsion-dispersion intermolecular potential is obtained from previous parametrizations on organic crystal structures. In order to calculate the free energies of hydration, water-solute repulsion-dispersion interactions are modeled using Lorenz-Berthelot combining rules. The root-mean-square error of the predicted hydration free energies is 1.5 kcal mol(-1), which is comparable to the error found using a continuum mean-field quantum mechanical approach parametrized using experimental free energy of hydration data. The results are also contrasted with explicit-solvent hydration free energies obtained with an atomic charge representation of the solute's charge density computed at the same level of theory used to compute the distributed multipoles. Replacing the multipole description of the solute's charge density with an atomic charge model changes the free energy of hydration by as much as 3 kcal mol(-1) and provides an estimate for the effect of the modeling quality of the intermolecular electrostatic forces in free energy of solvation calculations.

In silico ADME/T modelling for rational drug design

In recent decades, in silico absorption, distribution, metabolism, excretion (ADME), and toxicity (T) modelling as a tool for rational drug design has received considerable attention from pharmaceutical scientists, and various ADME/T-related prediction models have been reported. The high-throughput and low-cost nature of these models permits a more streamlined drug development process in which the identification of hits or their structural optimization can be guided based on a parallel investigation of bioavailability and safety, along with activity. However, the effectiveness of these tools is highly dependent on their capacity to cope with needs at different stages, e.g. their use in candidate selection has been limited due to their lack of the required predictability. For some events or endpoints involving more complex mechanisms, the current in silico approaches still need further improvement. In this review, we will briefly introduce the development of in silico models for some physicochemical parameters, ADME properties and toxicity evaluation, with an emphasis on the modelling approaches thereof, their application in drug discovery, and the potential merits or deficiencies of these models. Finally, the outlook for future ADME/T modelling based on big data analysis and systems sciences will be discussed.