Quantum chemical predictions of water–octanol partition coefficients applied to the SAMPL6 logP blind challenge

Theoretical approaches for predicting physicochemical properties are valuable tools for accelerating the drug discovery process. In this work, quantum chemical methods are used to predict water–octanol partition coefficients as a part of the SAMPL6 blind challenge. The SMD continuum solvent model was employed with MP2 and eight DFT functionals in conjunction with correlation consistent basis sets to determine the water–octanol transfer free energy. Several tactics towards improving the predictions of the partition coefficient were examined, including increasing the quality of basis sets, considering tautomerization, and accounting for inhomogeneities in the water and n-octanol phases. Evaluation of these various schemes highlights the impact of modeling approaches across different methods. With the inclusion of tautomers and adjustments to the permittivity constants, the best predictions were obtained with smaller basis sets and the O3LYP functional, which yielded an RMSE of 0.79 logP units. The results presented correspond to the SAMPL6 logP submission IDs: DYXBT, O7DJK, and AHMTF.

[1]  Giovanni Conti,et al.  Thermodynamic study of organic compounds in octan-1-ol. Processes of transfer from gas and from dilute aqueous solution , 1986 .

[2]  J. Guthrie,et al.  A blind challenge for computational solvation free energies: introduction and overview. , 2009, The journal of physical chemistry. B.

[3]  M. Head‐Gordon,et al.  Long-range corrected hybrid density functionals with damped atom-atom dispersion corrections. , 2008, Physical chemistry chemical physics : PCCP.

[4]  Bin Chen,et al.  Microscopic structure and solvation in dry and wet octanol. , 2006, The journal of physical chemistry. B.

[5]  W. Riebesehl,et al.  Thermodynamics of non-electrolyte transfer between octanol and water , 1986 .

[6]  Jaroslaw Polanski,et al.  Modeling Robust QSAR , 2006, J. Chem. Inf. Model..

[7]  Eugene N. Muratov,et al.  QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery , 2018, Front. Pharmacol..

[8]  D. Arthur,et al.  Review on: quantitative structure activity relationship (QSAR) modeling , 2018 .

[9]  Tao Wang,et al.  Quantitative structure–activity relationship: promising advances in drug discovery platforms , 2015, Expert opinion on drug discovery.

[10]  Parr,et al.  Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. , 1988, Physical review. B, Condensed matter.

[11]  Bernard R. Brooks,et al.  Absolute and relative pKa predictions via a DFT approach applied to the SAMPL6 blind challenge , 2018, Journal of Computer-Aided Molecular Design.

[12]  Giovanni Conti,et al.  Thermodynamic study of the partitioning of organic compounds between water and octan-1-ol. Effects of water as cosolvent in the organic phase , 1995 .

[13]  Andrew J. Dallas,et al.  A thermodynamic and solvatochromic investigation of the effect of water on the phase-transfer properties of octan-1-ol , 1992 .

[14]  Michael J. Frisch,et al.  A direct MP2 gradient method , 1990 .

[15]  Kee‐Chuan Pan,et al.  Hydrogen bonding in polar liquid solutions. 4. Effect of hydrogen-bonding solutes on dielectric constant and solvent structure in 1-octanol , 1976 .

[16]  A. Becke.  Density-functional thermochemistry. III. The role of exact exchange , 1993 .

[17]  C. Cramer,et al.  Universal solvation model based on solute electron density and on a continuum model of the solvent defined by the bulk dielectric constant and atomic surface tensions. , 2009, The journal of physical chemistry. B.

[18]  M. Frisch,et al.  Ab Initio Calculation of Vibrational Absorption and Circular Dichroism Spectra Using Density Functional Force Fields , 1994 .

[19]  Andreas Klamt,et al.  Prediction of cyclohexane-water distribution coefficients with COSMO-RS on the SAMPL5 data set , 2016, Journal of Computer-Aided Molecular Design.

[20]  Justin L MacCallum,et al.  Structures of neat and hydrated 1-octanol from computer simulations. , 2002, Journal of the American Chemical Society.

[21]  Giovanni Conti,et al.  Free-energy and Enthalpy Changes For the Process of Transfer From Gas and From Dilute Aqueous-solutions of Some Alkanes and Monofunctional Saturated Organic-compounds , 1991 .

[22]  Svein Saebo,et al.  Avoiding the integral storage bottleneck in LCAO calculations of electron correlation , 1989 .

[23]  David L. Mobley,et al.  The SAMPL4 host–guest blind prediction challenge: an overview , 2014, Journal of Computer-Aided Molecular Design.

[24]  Angela K. Wilson,et al.  Gaussian basis sets for use in correlated molecular calculations. X. The atoms aluminum through argon revisited , 2001 .

[25]  Uko Maran,et al.  Best Practices for QSAR Model Reporting: Physical and Chemical Properties, Ecotoxicity, Environmental Fate, Human Health, and Toxicokinetics Endpoints , 2018, Environmental health perspectives.

[26]  Brian E. Lang,et al.  Solubility of Water in Octan-1-ol from (275 to 369) K , 2012 .

[27]  N. Handy,et al.  Left-right correlation energy , 2001 .

[28]  Angela K. Wilson,et al.  Gaussian basis sets for use in correlated molecular calculations. IX. The atoms gallium through krypton , 1993 .

[29]  David L. Mobley,et al.  Octanol–water partition coefficient measurements for the SAMPL6 blind prediction challenge , 2019, bioRxiv.

[30]  D. Ritson,et al.  Dielectric Properties of Aqueous Ionic Solutions. Parts I and II , 1948 .

[31]  David L. Mobley,et al.  Blind prediction of cyclohexane–water distribution coefficients from the SAMPL5 challenge , 2016, Journal of Computer-Aided Molecular Design.

[32]  J. Westall,et al.  Distribution of lithium chloride, sodium chloride, potassium chloride, hydrochloric acid, magnesium chloride, and calcium chloride between octanol and water , 1990 .

[33]  A. Becke,et al.  Density-functional exchange-energy approximation with correct asymptotic behavior. , 1988, Physical review. A, General physics.

[34]  Donald G. Truhlar,et al.  Improving the Accuracy of Hybrid Meta-GGA Density Functionals by Range Separation , 2011 .

[35]  Bernard R. Brooks,et al.  Blind prediction of distribution in the SAMPL5 challenge with QM based protomer and pKa corrections , 2016, Journal of Computer-Aided Molecular Design.

[36]  Michael K. Gilson,et al.  Blind prediction of host–guest binding affinities: a new SAMPL3 challenge , 2012, Journal of Computer-Aided Molecular Design.

[37]  M. Head‐Gordon,et al.  Systematic optimization of long-range corrected hybrid density functionals. , 2008, The Journal of chemical physics.

[38]  S. Grimme.  Semiempirical hybrid density functional with perturbative second-order correlation. , 2006, The Journal of chemical physics.

[39]  Anthony Nicholls,et al.  The SAMPL2 blind prediction challenge: introduction and overview , 2010, J. Comput. Aided Mol. Des..

[40]  David L. Mobley,et al.  Blind prediction of solvation free energies from the SAMPL4 challenge , 2014, Journal of Computer-Aided Molecular Design.

[41]  Jorge Gálvez,et al.  Advances in the molecular modeling and quantitative structure–activity relationship-based design for antihistamines , 2013, Expert opinion on drug discovery.

[42]  Kee‐Chuan Pan,et al.  Hydrogen bonding in polar liquid solutions. 2. 1-Octanol in nonhydroxylic solvents , 1976 .

[43]  Matthew T. Geballe,et al.  The SAMPL3 blind prediction challenge: transfer energy overview , 2012, Journal of Computer-Aided Molecular Design.

[44]  Martin Head-Gordon,et al.  Analytic MP2 frequencies without fifth-order storage. Theory and application to bifurcated hydrogen bonds in the water hexamer , 1994 .

[45]  Pavel Polishchuk,et al.  Interpretation of Quantitative Structure-Activity Relationship Models: Past, Present, and Future , 2017, J. Chem. Inf. Model..

[46]  T. H. Dunning.  Gaussian basis sets for use in correlated molecular calculations. I. The atoms boron through neon and hydrogen , 1989 .

[47]  Jan Andzelm,et al.  Gaussian Basis Sets for Molecular Calculations , 2012 .

[48]  V. Barone,et al.  Toward reliable density functional methods without adjustable parameters: The PBE0 model , 1999 .

[49]  Paola Sassi,et al.  Water/Alcohol Mixtures: A Spectroscopic Study of the Water-Saturated 1-Octanol Solution , 2004 .

[50]  Amanda G. Riojas,et al.  Solv-ccCA: Implicit Solvation and the Correlation Consistent Composite Approach for the Determination of pKa. , 2014, Journal of chemical theory and computation.

[51]  A. Geoffrey Skillman.  SAMPL3: blinded prediction of host–guest binding affinities, hydration free energies, and trypsin inhibitors , 2012, Journal of Computer-Aided Molecular Design.

[52]  Xiao Wang,et al.  pKa measurements for the SAMPL6 prediction challenge for a set of kinase inhibitor-like fragments , 2018, bioRxiv.

[53]  Bernard R Brooks,et al.  Partition coefficients for the SAMPL5 challenge using transfer free energies , 2016, Journal of Computer-Aided Molecular Design.

[54]  N. Gavish,et al.  Dependence of the dielectric constant of electrolyte solutions on ionic concentration: A microfield approach. , 2012, Physical review. E.

[55]  Michael J. Frisch,et al.  MP2 energy evaluation by direct methods , 1988 .

[56]  Chris Morley,et al.  Open Babel: An open chemical toolbox , 2011, J. Cheminformatics.

[57]  Denis Fourches,et al.  4D- quantitative structure–activity relationship modeling: making a comeback , 2019, Expert opinion on drug discovery.

[58]  Bernard R. Brooks,et al.  Calculating distribution coefficients based on multi-scale free energy simulations: an evaluation of MM and QM/MM explicit solvent simulations of water-cyclohexane transfer in the SAMPL5 challenge , 2016, Journal of Computer-Aided Molecular Design.