Literature DB >> 24706106

SAMPL4, a blind challenge for computational solvation free energies: the compounds considered.

J Peter Guthrie1.   

Abstract

For the fifth time I have provided a set of solvation energies (1 M gas to 1 M aqueous) for a SAMPL challenge. In this set there are 23 blind compounds and 30 supplementary compounds of related structure to one of the blind sets, but for which the solvation energy is readily available. The best current values of each compound are presented along with complete documentation of the experimental origins of the solvation energies. The calculations needed to go from reported data to solvation energies are presented, with particular attention to aspects which are new to this set. For some compounds the vapor pressures (VP) were reported for the liquid compound, which is solid at room temperature. To correct from VPsubcooled liquid to VPsublimation requires ΔSfusion, which is only known for mannitol. Estimated values were used for the others, all but one of which were benzene derivatives and expected to have very similar values. The final compound for which ΔSfusion was estimated was menthol, which melts at 42 °C so that modest errors in ΔSfusion will have little effect. It was also necessary to look into the effects of including estimated values of ΔCp on this correction. The approximate sizes of the effects of inclusion of ΔCp in the correction from VPsubcooled liquid to VPsublimation were estimated and it was noted that inclusion of ΔCp invariably makes ΔGS more positive. To extend the set of compounds for which the solvation energy could be calculated we explored the use of boiling point (b.p.) data from Reaxys/Beilstein as a substitute for studies of the VP as a function of temperature. B.p. data are not always reliable so it was necessary to develop a criterion for rejecting outliers. For two compounds (chlorinated guaiacols) it became clear that inclusion represented overreach; for each there were only two independent pressure, temperature points, which is too little for a trustworthy extrapolation. For a number of compounds the extrapolation from lowest temperature at which the VP was reported to 25 °C was long (sometimes over 100°) so that it was necessary to consider whether ΔCp might have significant effects. The problem is that there are no experimental values and possible intramolecular hydrogen bonds make estimation uncertain in some cases. The approximate sizes of the effects of ΔCp were estimated, and it was noted that inclusion of ΔCp in the extrapolation of VP down to room temperature invariably makes ΔGs more negative.

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Year:  2014        PMID: 24706106     DOI: 10.1007/s10822-014-9738-y

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  30 in total

1.  Absorption classification of oral drugs based on molecular surface properties.

Authors:  Christel A S Bergström; Melissa Strafford; Lucia Lazorova; Alex Avdeef; Kristina Luthman; Per Artursson
Journal:  J Med Chem       Date:  2003-02-13       Impact factor: 7.446

2.  The SAMPL2 blind prediction challenge: introduction and overview.

Authors:  Matthew T Geballe; A Geoffrey Skillman; Anthony Nicholls; J Peter Guthrie; Peter J Taylor
Journal:  J Comput Aided Mol Des       Date:  2010-05-09       Impact factor: 3.686

3.  Solubility challenge: can you predict solubilities of 32 molecules using a database of 100 reliable measurements?

Authors:  Antonio Llinàs; Robert C Glen; Jonathan M Goodman
Journal:  J Chem Inf Model       Date:  2008-07-15       Impact factor: 4.956

4.  A blind challenge for computational solvation free energies: introduction and overview.

Authors:  J Peter Guthrie
Journal:  J Phys Chem B       Date:  2009-04-09       Impact factor: 2.991

Review 5.  Molecular recognition and binding free energy calculations in drug development.

Authors:  B N Dominy
Journal:  Curr Pharm Biotechnol       Date:  2008-04       Impact factor: 2.837

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7.  Vapour pressures, aqueous solubilities, Henry's law constants, partition coefficients between gas/water (Kgw), n-octanol/water (Kow) and gas/n-octanol (Kgo) of 106 polychlorinated diphenyl ethers (PCDE)

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Journal:  Chemosphere       Date:  1999-02       Impact factor: 7.086

8.  Absolute configuration of a new mosquito repellent, (+)-eucamalol and the repellent activity of its epimer.

Authors:  A Satoh; H Utamura; T Nakade; H Nishimura
Journal:  Biosci Biotechnol Biochem       Date:  1995-06       Impact factor: 2.043

9.  Influences of solvent on group transfer potentials and biochemical recognition of carbohydrates. Anomalous solvation of the anomeric hydroxyl group.

Authors:  R Wolfenden; Y L Liang
Journal:  J Biol Chem       Date:  1988-06-15       Impact factor: 5.157

10.  [Synthetic ataractics. II. 1-(Aminoalkyl)- and 1-(aminoalkylidene)-2,3:6,7-dibenzosuberane].

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  30 in total

1.  Presenting data in such a fashion that they can be used by other scientists.

Authors:  J Peter Guthrie
Journal:  J Comput Aided Mol Des       Date:  2015-09-22       Impact factor: 3.686

2.  pKa measurements for the SAMPL6 prediction challenge for a set of kinase inhibitor-like fragments.

Authors:  Mehtap Işık; Dorothy Levorse; Ariën S Rustenburg; Ikenna E Ndukwe; Heather Wang; Xiao Wang; Mikhail Reibarkh; Gary E Martin; Alexey A Makarov; David L Mobley; Timothy Rhodes; John D Chodera
Journal:  J Comput Aided Mol Des       Date:  2018-11-07       Impact factor: 3.686

3.  Performance of the SMD and SM8 models for predicting solvation free energy of neutral solutes in methanol, dimethyl sulfoxide and acetonitrile.

Authors:  Caroline C Zanith; Josefredo R Pliego
Journal:  J Comput Aided Mol Des       Date:  2014-11-15       Impact factor: 3.686

4.  LigParGen web server: an automatic OPLS-AA parameter generator for organic ligands.

Authors:  Leela S Dodda; Israel Cabeza de Vaca; Julian Tirado-Rives; William L Jorgensen
Journal:  Nucleic Acids Res       Date:  2017-07-03       Impact factor: 16.971

5.  Octanol-water partition coefficient measurements for the SAMPL6 blind prediction challenge.

Authors:  Mehtap Işık; Dorothy Levorse; David L Mobley; Timothy Rhodes; John D Chodera
Journal:  J Comput Aided Mol Des       Date:  2019-12-19       Impact factor: 3.686

6.  Efficient calculation of SAMPL4 hydration free energies using OMEGA, SZYBKI, QUACPAC, and Zap TK.

Authors:  Benjamin A Ellingson; Matthew T Geballe; Stanislaw Wlodek; Christopher I Bayly; A Geoffrey Skillman; Anthony Nicholls
Journal:  J Comput Aided Mol Des       Date:  2014-03-16       Impact factor: 3.686

7.  Predicting hydration free energies with a hybrid QM/MM approach: an evaluation of implicit and explicit solvation models in SAMPL4.

Authors:  Gerhard König; Frank C Pickard; Ye Mei; Bernard R Brooks
Journal:  J Comput Aided Mol Des       Date:  2014-02-07       Impact factor: 3.686

8.  Predicting hydration free energies with chemical accuracy: the SAMPL4 challenge.

Authors:  Lars Sandberg
Journal:  J Comput Aided Mol Des       Date:  2014-02-19       Impact factor: 3.686

9.  An efficient protocol for obtaining accurate hydration free energies using quantum chemistry and reweighting from molecular dynamics simulations.

Authors:  Frank C Pickard; Gerhard König; Andrew C Simmonett; Yihan Shao; Bernard R Brooks
Journal:  Bioorg Med Chem       Date:  2016-08-22       Impact factor: 3.641

10.  Computation of Hydration Free Energies Using the Multiple Environment Single System Quantum Mechanical/Molecular Mechanical Method.

Authors:  Gerhard König; Ye Mei; Frank C Pickard; Andrew C Simmonett; Benjamin T Miller; John M Herbert; H Lee Woodcock; Bernard R Brooks; Yihan Shao
Journal:  J Chem Theory Comput       Date:  2015-12-11       Impact factor: 6.006

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