A Blind Challenge for Computational Solvation Free Energies: Introduction and Overview

Guthrie, J. Peter

doi:10.1021/jp806724u

Cited by 209 publications

(336 citation statements)

References 15 publications

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“…11 We will start by calibrating an empirical term for the cost of cavity formation in water, which is not directly provided by the force-field energy terms in the LIE approach. The LIE results for the case of flexible solutes and the AM1BCC-SP charge model will be described first vis-à-vis experimental data.…”

Section: Resultsmentioning

confidence: 99%

“…The testing data set mirrors the training data set in terms of chemical class representation for monofunctional compounds but differs from the training analogs by having increased flexibility and containing a larger collection of polyfunctional compounds. The SAMPL1 data set 11 consists of 63 druglike, diverse, polyfunctional, neutral polar compounds and spans a wider range of transfer free energies and molecular weights in comparison to the training and testing data sets. Details on the composition of the training and testing and SAMPL1 data sets are provided in the Supporting Information (Table S1).…”

Section: Methodsmentioning

confidence: 99%

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Rapid Prediction of Solvation Free Energy. 1. An Extensive Test of Linear Interaction Energy (LIE)

Sulea

Corbeil

Purisima

2010

J. Chem. Theory Comput.

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Abstract:The present study provides a comprehensive systematic analysis on the applicability of the linear interaction energy (LIE) approximation to the prediction of gas-to-water transfer (hydration) free energy. The study is based on molecular dynamics simulations in explicit solvent for an extensive and diverse hydration data set comprising 564 neutral compounds with measured hydration free energies, including a "traditional" data set and the more challenging drug-like SAMPL1 data set. A highly correlative LIE model was achieved without empirical scaling of the solute-solvent interaction energy terms along with a cavity term calibrated to the experiment. This model was particularly accurate for the "traditional" data set and of acceptable accuracy for the SAMPL1 data set, with mean-unsigned-errors below 1 kcal/mol and slightly above 2 kcal/mol, respectively. We have analyzed the sensitivity of the LIE model to several parameters such as continuum correction terms applied outside the explicit water shell, the impact of various charging methods, the applicability of single-conformer representation of the solute, and the inclusion of internal energy terms. The parameters with the greatest sensitivity are the charging methods used, with AM1BCC-SP (without AM1 geometry optimization) charges favored over AM1BCC-OPT and RESP charges. The inclusion of the change in intramolecular van der Waals and electrostatic energies between the solution and gas phases can also lead to improved prediction accuracies. Functional group based error analysis identified several chemical classes as minor outliers with systematic errors. A direct comparison of the LIE and free energy perturbation (FEP) approaches using the same force field and charging method shows that the LIE approximation is at least as accurate as the FEP approach with a reduction of computing time by at least 1 order of magnitude.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Rapid Prediction of Solvation Free Energy. 1. An Extensive Test of Linear Interaction Energy (LIE)

Sulea

Corbeil

Purisima

2010

J. Chem. Theory Comput.

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“…Their trisubstituted pyrimidine and triazine moieties can also be found in SAMPL-1 compounds 24 and 50, both with hydration free energies predicted very well in the prospective study. The phenylsulfone moiety, present in three of the sulfoneurea outliers and similar to the benzylsulfone and (12) in the prospective SAMPL-1 predictions based on a 16-atom-type definition (err1) and a 25-atom-type definition (err2) of the continuum dispersion model, at D in ) 1 and F)0.9. The outliers are drawn in the center.…”

Section: Resultsmentioning

confidence: 97%

“…A subset of 129 compounds constituted a training data set used for calibrating the continuum solute-solvent Lennard-Jones atom-type coefficients, B i , in eq 1. In the training set, we included mostly rigid representatives of the various chemical classes, with the majority of compounds (117) being monofunctional, and only a few polyfunctional compounds (12) were included to increase coverage of functional groups. The remaining 166 compounds formed the test data set, which mirrors the training data set in terms of chemical class representation for monofunctional compounds (117) but differ from the training analogs by having increased flexibility and containing a larger collection of polyfunctional compounds (49).…”

Section: Methodsmentioning

confidence: 99%

Prediction of SAMPL-1 Hydration Free Energies Using a Continuum Electrostatics-Dispersion Model

et al. 2009

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The SAMPL-1 hydration free energy blind prediction challenge data set includes 63 compounds that are more chemically diverse, polyfunctional, drug-like, and with examples of transfer free energies and molecular weights larger than ever before seen in previously tabulated data sets of neutral compounds. For the prospective SAMPL-1 study, we employed a continuum model including a boundary element solution of the Poisson equation to describe electrostatic solvation, a molecular surface area-based cost of cavity formation in water, and a continuum Lennard-Jones potential to account for dispersion-repulsion solute-solvent effects. For the latter contribution, continuum van der Waals atom-type coefficients were calibrated and validated on previously available hydration data sets. In the prospective study, this continuum hydration model yielded SAMPL-1 predictions highly correlated with experimental data, albeit with a slope of slightly above 0.5, suggesting a valid model but with a systematic error. Analysis of the major outliers, all overestimating the experimental hydration data, highlights a common structural theme as a possible cause of the prediction errors: densely polar and hydrogen-bond-capable structures, featuring primarily substituted (sulfon)amide groups, often in conjugated systems. By examining analog pairs within the SAMPL-1 data set, it was also noted that certain solvation trends are captured neither by chemical sense nor by our hydration model, which seem too additive. A retrospective analysis of model transferability between hydration data sets as a function of its parameters and complexity indicates that the electrostatic component of the model is fairly transferrable across data sets, but the nonelectrostatic terms are less so. For the chemical space covered in SAMPL-1, absolute prediction errors indicate that the simpler transferrable electrostatics-only model outperforms the more complex model including cavity and continuum dispersion terms. Possible directions to further improve this continuum hydration model are proposed.

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“…The set of 167 reference compounds used previously contained a combination of alcohols, alkanes, cycloalkanes, alkenes, alkynes, alkyl benzenes, amines, amides, aldehydes, carboxylic acids, esters, ketones, thiols and sulphides and included molecules from the earlier SAMPL0/CUP8, SAMPL1 and SAMPL2 challenges. [141][142][143] The other 47 molecules formed part of the SAMPL4 challenge and are listed in Table 5.1. Topologies for all the molecules used in this study are publicly available via the ATB repository.…”

Section: Resultsmentioning

confidence: 99%

Improving the accuracy of molecular dynamics simulations: parameterisation of interaction potentials for small molecules

Stroet¹

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The ability to accurately describe molecular systems with classical molecular dynamics (MD) is critically dependent on an understanding of the sources of error in the design, evaluation and analysis of the underlying model. One significant source of error is in the degree to which the equations and parameters used to represent atomic interaction can reproduce the experimental properties relevant to a particular application. While highly optimised and well-validated interaction parameters are available for common biomolecules (amino acids, sugars, lipids etc.), this is not the case for heterogeneous small molecules such as co-factors, substrates and drugs. The enormity of the chemical space covered by small molecules necessitates the development of automated approaches to parameterising and validating interaction parameters. Several tools are available that can be used to generate interaction parameters for small molecules compatible with a particular existing (bio)molecular force field. Despite their popularity, many are poorly validated, while some have been shown to be inappropriate for many applications. In other cases, validation studies have demonstrated reasonable performance, however, they also suggest significant improvements can be made. One such tool is the Automated Topology Builder (ATB, http://atb.uq.edu.au/) which provides interaction parameters for small molecules compatible with the GROMOS biomolecular force field.As part of this work, a fully automated protocol for the calculation of solvation energy by thermodynamic integration has been developed and applied to the large-scale validation of the ATB against experimental solvation data in water and hexane. It is shown that the largest errors are due to the Lennard-Jones parameters used for functional groups not present in common biomolecules. Other sources of error included the atomic charges assigned by the ATB for the united atom carbons and incompatibilities between GROMOS Lennard-Jones parameters and the ATB charge model. Significant sources of error were also identified in the evaluation of MD models, both for the calculation of solvation energy as well as for MD simulations of lipid membranes. In particular, the use of multiple-time-step algorithms as a time-saving technique. For the calculation of solvation energy by TI, the twin-range cutoff scheme-commonly used in simulations with the GROMOS force field-was found to introduce a systematic error of about 1 kJ/mol. While for lipid systems, various III changes made to the integration algorithm of the GROMACS simulation code were found to significantly alter the properties of lipid membranes when simulated with identical force fields.Finally, a method for parameterising transferrable interaction potentials with respect to a wide range of target properties is presented. The method is based on an analysis of surfaces corresponding to the difference between calculated and target data as a function of alternative combinations of parameters.The consideration of surfaces in parameter space...

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A Blind Challenge for Computational Solvation Free Energies: Introduction and Overview

Cited by 209 publications

References 15 publications

Rapid Prediction of Solvation Free Energy. 1. An Extensive Test of Linear Interaction Energy (LIE)

Rapid Prediction of Solvation Free Energy. 1. An Extensive Test of Linear Interaction Energy (LIE)

Prediction of SAMPL-1 Hydration Free Energies Using a Continuum Electrostatics-Dispersion Model

Improving the accuracy of molecular dynamics simulations: parameterisation of interaction potentials for small molecules

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