Approaches for Calculating Solvation Free Energies and Enthalpies Demonstrated with an Update of the FreeSolv Database

Matos, Guilherme Duarte Ramos; Kyu, Daisy Y.; Loeffler, Hannes H.; Chodera, John D.; Shirts, Michael R.; Mobley, David L.

doi:10.1021/acs.jced.7b00104

Cited by 209 publications

(364 citation statements)

References 146 publications

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“…There have been a number of studies aimed at examining the accuracy of MM force fields by computing the solvation free energy for a large set of compounds 46, 52-55 . What distinguishes the present effort with these previous studies is that the calculated solvation free energy here are based on the GAAMP charges and dihedral potentials combined with a set of LJ parameters specifically optimized to fit the properties of the neat liquids.…”

Section: Resultsmentioning

confidence: 99%

Optimized Lennard-Jones Parameters for Druglike Small Molecules

Boulanger

Huang

Rupakheti

et al. 2018

J. Chem. Theory Comput.

107

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Meaningful efforts in computer-aided drug design (CADD) need accurate molecular mechanical force fields to quantitatively characterize protein-ligand interactions, ligand hydration free energies and other ligand physical properties. Atomic models of new compounds are commonly generated by analogy from the pre-defined tabulated parameters of a given force field. Two widely used approaches following this strategy are the General Amber Force Field, GAFF, and the CHARMM General Force Field (CGenFF). An important limitation of using pre-tabulated parameter values is that they may be inadequate in the context of a specific molecule. To resolve this issue, we previously introduced the General Automated Atomic Model Parameterization (GAAMP) for automatically generating the parameters of atomic models of small molecules using the results from ab initio quantum mechanical (QM) calculations as target data. The GAAMP protocol uses QM data to optimize the bond, valence angle, and dihedral angle internal parameters, and atomic partial charges. However, because the treatment of van der Waals interactions based on QM is challenging and may often be unreliable, the Lennard-Jones 6-12 parameters are kept unchanged from the initial atom types assignments (GAFF or CGenFF), which limits the accuracy that can be achieved by these models. To address this issue, a new set of van der Waals Lennard-Jones 6-12 parameters was systematically optimized to reproduce experimental neat liquid densities and enthalpies of vaporization for a large set of 430 compounds covering a wide range of chemical functionalities. Calculations of the hydration free energy indicate that optimal accuracy for these models is achieved when the molecule-water van der Waals dispersion is rescaled by a factor of 1.115. The final optimized model yields an average unsigned error in the hydration free energies of 0.79 kcal/mol.

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Section: Resultsmentioning

confidence: 99%

Optimized Lennard-Jones Parameters for Druglike Small Molecules

Boulanger

Huang

Rupakheti

et al. 2018

J. Chem. Theory Comput.

107

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“…We obtained the excess chemical potentials from solvation free energy calculations 24, 28, 54 . Volumes were obtained from the state in the alchemical path where the solute was fully coupled to the rest of the system.…”

Section: Resultsmentioning

confidence: 99%

“…While all-atom simulations can yield solubility estimates given adequate simulation time and a correct method, the computed solubility will be that dictated by the underlying energy model or force field, and will not necessarily match experiment. Here, we use GAFF, a general-purpose force field with known limitations 28, 71, 86, 87 ; apparently, here, the right answer for the force field is not correct. Perhaps this is because of limitations in describing the solid state, as the force field is parameterized for liquid state simulations.…”

Section: Resultsmentioning

confidence: 99%

“…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%

“…physical interactions that differ from those given by the ideal gas law). We obtained excess chemical potentials from solvation free energy calculations; the solute molecule is inserted in the solution by progressively turning on its interactions with the surrounding environment 24, 28, 54 .…”

Section: Theorymentioning

confidence: 99%

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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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