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

Bannan, Caitlin C.; Calabró, Gaetano; Kyu, Daisy Y.; Mobley, David L.

doi:10.1021/acs.jctc.6b00449

Cited by 167 publications

(179 citation statements)

References 111 publications

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“…The model uncertainty, on the other hand, was intended to provide an estimate of how well the calculated value will agree with experiment. For example, in a recent study we computed cyclohexane/water partition coefficients using alchemical solvation free energy calculations in GROMACS where the statistical uncertainties were around 0.05 log units, but the root mean squared error was around 1.4 log units [23], so an appropriate estimated model uncertainty would have been 1.4 log units. A careful analysis of expected error could even yield model uncertainties which would vary based on the anticipated difficulty or complexity of a compound.…”

Section: Challenge Logisticsmentioning

confidence: 99%

“…As demonstrated in the literature, [11–21] partition coefficients are directly proportional to the difference between the solvation free energy for the solute into each solvent. We use previously established and automated protocols [23] to calculate the solvation free energy of each molecule into water and cyclohexane. Then the calculated partition coefficient was reported as an estimate for log D .…”

Section: Reference Calculationsmentioning

confidence: 99%

“…The TIP3P water model [35] was used for the aqueous phase. Topology and coordinate files for the solvated boxes with 1 solute molecule and 500 cyclohexane or 1000 water molecules were built using our Solvation Toolkit [23]. These files were then converted to AMBER, DESMOND, and LAMMPS formats and provided to SAMPL5 participants as reference calculations.…”

Section: Reference Calculationsmentioning

confidence: 99%

“…It converts SMILES strings or IUPAC names of any mixture of small organic compounds to parameterized molecules and builds topology and coordinate files for a variety of simulation packages. All molecular dynamics parameters are identical to previous studies [36, 4, 23]. The molecule is taken from the solvated box to a non-interacting gas phase in 20 lambda values.…”

Section: Reference Calculationsmentioning

confidence: 99%

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

Bannan

Burley

Chiu

et al. 2016

J Comput Aided Mol Des

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122

114

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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.

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Section: Challenge Logisticsmentioning

confidence: 99%

Section: Reference Calculationsmentioning

confidence: 99%

Section: Reference Calculationsmentioning

confidence: 99%

Section: Reference Calculationsmentioning

confidence: 99%

See 2 more Smart Citations

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

Bannan

Burley

Chiu

et al. 2016

J Comput Aided Mol Des

Self Cite

122

114

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show abstract

“…Liquid state simulation boxes were generated using the SolvationToolkit 77 , a Python package that uses packmol 78 , OpenMolTools (v0.6.7) 79 and OpenEye Python Toolkits 80– 82 . Excess chemical potentials were obtained with the same solvation free energy protocol used in previous studies 28 : Starting from a fully interacting system, we progressively decouple the interactions of a single solute molecule with the remaining of the system, which allows us to calculate the free energy difference between a solute molecule in vacuum and in solution (i.e., the solvation free energy).…”

Section: Methodsmentioning

confidence: 99%

Challenges in the use of atomistic simulations to predict solubilities of drug-like molecules

Matos

Mobley

2018

F1000Res

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Background: Solubility is a physical property of high importance to the pharmaceutical industry, the prediction of which for potential drugs has so far been a hard task. We attempted to predict the solubility of acetylsalicylic acid (ASA) by estimating the absolute chemical potentials of its most stable polymorph and of solutions with different concentrations of the drug molecule. Methods: Chemical potentials were estimated from all-atom molecular dynamics simulations. We used the Einstein molecule method (EMM) to predict the absolute chemical potential of the solid and solvation free energy calculations to predict the excess chemical potentials of the liquid-phase systems. Results: Reliable estimations of the chemical potentials for the solid and for a single ASA molecule using the EMM required an extremely large number of intermediate states for the free energy calculations, meaning that the calculations were extremely demanding computationally. Despite the computational cost, however, the computed value did not agree well with the experimental value, potentially due to limitations with the underlying energy model. Perhaps better values could be obtained with a better energy model; however, it seems likely computational cost may remain a limiting factor for use of this particular approach to solubility estimation. Conclusions: Solubility prediction of drug-like solids remains computationally challenging, and it appears that both the underlying energy model and the computational approach applied may need improvement before the approach is suitable for routine use.

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Formulation Development

2023

Drug Compounding for Veterinary Professionals

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

Cited by 167 publications

References 111 publications

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

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

Challenges in the use of atomistic simulations to predict solubilities of drug-like molecules

Formulation Development

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