Prediction of cyclohexane-water distribution coefficients with COSMO-RS on the SAMPL5 data set

The Conductor-Like-Screening-Model for Real Solvents (COSMO-RS) method has been used for the blind prediction of cyclohexane-water distribution coefficients logD within the SAMPL challenge. The partition coefficient logP of the neutral species was calculated first and then corrected for dissociation or protonation, as appropriate for acidic or basic solutes, to obtain the cyclohexane-water logD. Using the latest version of the COSMOtherm implementation, this approach in combination with a rigorous conformational sampling yielded a predictive accuracy of 2.11 log units (RMSD) for the 53 compounds of the blind prediction dataset. By that it was the most accurate of all contest submissions and it also achieved the best rank order. The RMSD mainly arises from a group of outliers in the negative logD range, which at least partly may arise from dimerization or other experimental problems coming up for very polar molecules in very non-polar solvents.     

Blinded predictions of distribution coefficients in the SAMPL5 challenge

In the context of the SAMPL5 challenge water-cyclohexane distribution coefficients for 53 drug-like molecules were predicted. Four different models based on molecular dynamics free energy calculations were tested. All models initially assumed only one chemical state present in aqueous or organic phases. Model A is based on results from an alchemical annihilation scheme; model B adds a long range correction for the Lennard Jones potentials to model A; model C adds charging free energy corrections; model D applies the charging correction from model C to ionizable species only. Model A and B perform better in terms of mean-unsigned error (\(\hbox {MUE}=6.79<6.87<6.95 \log\) D units ? 95 % confidence interval) and determination coefficient \((\hbox {R}^2 = 0.26< 0.27< 0.28)\), while charging corrections lead to poorer results with model D (\(\hbox {MUE}=12.8<12.63<12.98\) and \(\hbox {R}^2 = 0.16<0.17<0.18\)). Because overall errors were large, a retrospective analysis that allowed co-existence of ionisable and neutral species of a molecule in aqueous phase was investigated. This considerably reduced systematic errors (\(\hbox {MUE}=1.87<1.97<2.07\) and \(\hbox {R}^2 = 0.35<0.40<0.45\)). Overall accurate \(\log D\) predictions for drug-like molecules that may adopt multiple tautomers and charge states proved difficult, indicating a need for methodological advances to enable satisfactory treatment by explicit-solvent molecular simulations.     

Prediction of cyclohexane-water distribution coefficients for the SAMPL5 data set using molecular dynamics simulations with the OPLS-AA force field

All-atom molecular dynamics simulations were used to predict water-cyclohexane distribution coefficients \(D_{cw}\) of a range of small molecules as part of the SAMPL5 blind prediction challenge. Molecules were parameterized with the transferable all-atom OPLS-AA force field, which required the derivation of new parameters for sulfamides and heterocycles and validation of cyclohexane parameters as a solvent. The distribution coefficient was calculated from the solvation free energies of the compound in water and cyclohexane. Absolute solvation free energies were computed by an established protocol using windowed alchemical free energy perturbation with thermodynamic integration. This protocol resulted in an overall root mean square error in \(\log D_{cw}\) of almost 4 log units and an overall signed error of ?3 compared to experimental data. There was no substantial overall difference in accuracy between simulating in NVT and NPT ensembles. The signed error suggests a systematic error but the experimental \(D_{cw}\) data on their own are insufficient to uncover the source of this error. Preliminary work suggests that the major source of error lies in the hydration free energy calculations.     

Partition coefficients for the SAMPL5 challenge using transfer free energies

SAMPL challenges (Mobley et al. in J Comput Aided Mol Des 28:135–150, 2014; Skillman in J Comput Aided Mol Des 26:473–474, 2012; Geballe in J Comput Aided Mol Des 24:259–279, 2010; Guthrie in J Phys Chem B 113:4501–4507, 2009) provide excellent opportunities to assess theoretical approaches on new data sets with a goal of gaining greater insight towards protein and ligand modeling. In the SAMPL5 experiment, cyclohexane–water partition coefficients were determined using a vertical solvation scheme in conjunction with the SMD continuum solvent model. Several DFT functionals partnered with correlation consistent basis sets were evaluated for the prediction of the partition coefficients. The approach chosen for the competition, a B3PW91 vertical solvation scheme, yields a mean absolute deviation of 1.9 logP units and performs well at estimating the correct hydrophilicity and hydrophobicity for the full SAMPL5 molecule set.     

Extended solvent-contact model approach to blind SAMPL5 prediction challenge for the distribution coefficients of drug-like molecules

The performance of the extended solvent-contact model has been addressed in the SAMPL5 blind prediction challenge for distribution coefficient (LogD) of drug-like molecules with respect to the cyclohexane/water partitioning system. All the atomic parameters defined for 41 atom types in the solvation free energy function were optimized by operating a standard genetic algorithm with respect to water and cyclohexane solvents. In the parameterizations for cyclohexane, the experimental solvation free energy (ΔG _sol ) data of 15 molecules for 1-octanol were combined with those of 77 molecules for cyclohexane to construct a training set because ΔG _sol values of the former were unavailable for cyclohexane in publicly accessible databases. Using this hybrid training set, we established the LogD prediction model with the correlation coefficient (R), average error (AE), and root mean square error (RMSE) of 0.55, 1.53, and 3.03, respectively, for the comparison of experimental and computational results for 53 SAMPL5 molecules. The modest accuracy in LogD prediction could be attributed to the incomplete optimization of atomic solvation parameters for cyclohexane. With respect to 31 SAMPL5 molecules containing the atom types for which experimental reference data for ΔG _sol were available for both water and cyclohexane, the accuracy in LogD prediction increased remarkably with the R, AE, and RMSE values of 0.82, 0.89, and 1.60, respectively. This significant enhancement in performance stemmed from the better optimization of atomic solvation parameters by limiting the element of training set to the molecules with experimental ΔG _sol data for cyclohexane. Due to the simplicity in model building and to low computational cost for parameterizations, the extended solvent-contact model is anticipated to serve as a valuable computational tool for LogD prediction upon the enrichment of experimental ΔG _sol data for organic solvents.     

Predicting cyclohexane/water distribution coefficients for the SAMPL5 challenge using MOSCED and the SMD solvation model

We present blind predictions using the solubility parameter based method MOSCED submitted for the SAMPL5 challenge on calculating cyclohexane/water distribution coefficients at 298 K. Reference data to parameterize MOSCED was generated with knowledge only of chemical structure by performing solvation free energy calculations using electronic structure calculations in the SMD continuum solvent. To maintain simplicity and use only a single method, we approximate the distribution coefficient with the partition coefficient of the neutral species. Over the final SAMPL5 set of 53 compounds, we achieved an average unsigned error of \(2.2\pm 0.2\) log units (ranking 15 out of 62 entries), the correlation coefficient (R) was \(0.6\pm 0.1\) (ranking 35), and \(72\pm 6\,\%\) of the predictions had the correct sign (ranking 30). While used here to predict cyclohexane/water distribution coefficients at 298 K, MOSCED is broadly applicable, allowing one to predict temperature dependent infinite dilution activity coefficients in any solvent for which parameters exist, and provides a means by which an excess Gibbs free energy model may be parameterized to predict composition dependent phase-equilibrium.     

Prediction of cyclohexane-water distribution coefficient for SAMPL5 drug-like compounds with the QMPFF3 and ARROW polarizable force fields

We present the performance of blind predictions of water—cyclohexane distribution coefficients for 53 drug-like compounds in the SAMPL5 challenge by three methods currently in use within our group. Two of them utilize QMPFF3 and ARROW, polarizable force-fields of varying complexity, and the third uses the General Amber Force-Field (GAFF). The polarizable FF’s are implemented in an in-house MD package, Arbalest. We find that when we had time to parametrize the functional groups with care (batch 0), the polarizable force-fields outperformed the non-polarizable one. Conversely, on the full set of 53 compounds, GAFF performed better than both QMPFF3 and ARROW. We also describe the torsion-restrain method we used to improve sampling of molecular conformational space and thus the overall accuracy of prediction. The SAMPL5 challenge highlighted several drawbacks of our force-fields, such as our significant systematic over-estimation of hydrophobic interactions, specifically for alkanes and aromatic rings.     

All-atom/coarse-grained hybrid predictions of distribution coefficients in SAMPL5

We present blind predictions submitted to the SAMPL5 challenge on calculating distribution coefficients. The predictions were based on estimating the solvation free energies in water and cyclohexane of the 53 compounds in the challenge. These free energies were computed using alchemical free energy simulations based on a hybrid all-atom/coarse-grained model. The compounds were treated with the general Amber force field, whereas the solvent molecules were treated with the Elba coarse-grained model. Considering the simplicity of the solvent model and that we approximate the distribution coefficient with the partition coefficient of the neutral species, the predictions are of good accuracy. The correlation coefficient, R is 0.64, 82 % of the predictions have the correct sign and the mean absolute deviation is 1.8 log units. This is on a par with or better than the other simulation-based predictions in the challenge. We present an analysis of the deviations to experiments and compare the predictions to another submission that used all-atom solvent.     

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

One of the central aspects of biomolecular recognition is the hydrophobic effect, which is experimentally evaluated by measuring the distribution coefficients of compounds between polar and apolar phases. We use our predictions of the distribution coefficients between water and cyclohexane from the SAMPL5 challenge to estimate the hydrophobicity of different explicit solvent simulation techniques. Based on molecular dynamics trajectories with the CHARMM General Force Field, we compare pure molecular mechanics (MM) with quantum-mechanical (QM) calculations based on QM/MM schemes that treat the solvent at the MM level. We perform QM/MM with both density functional theory (BLYP) and semi-empirical methods (OM1, OM2, OM3, PM3). The calculations also serve to test the sensitivity of partition coefficients to solute polarizability as well as the interplay of the quantum-mechanical region with the fixed-charge molecular mechanics environment. Our results indicate that QM/MM with both BLYP and OM2 outperforms pure MM. However, this observation is limited to a subset of cases where convergence of the free energy can be achieved.     

Blinded predictions of host-guest standard free energies of binding in the SAMPL5 challenge

In the context of the SAMPL5 blinded challenge standard free energies of binding were predicted for a dataset of 22 small guest molecules and three different host molecules octa-acids (OAH and OAMe) and a cucurbituril (CBC). Three sets of predictions were submitted, each based on different variations of classical molecular dynamics alchemical free energy calculation protocols based on the double annihilation method. The first model (model A) yields a free energy of binding based on computed free energy changes in solvated and host-guest complex phases; the second (model B) adds long range dispersion corrections to the previous result; the third (model C) uses an additional standard state correction term to account for the use of distance restraints during the molecular dynamics simulations. Model C performs the best in terms of mean unsigned error for all guests (MUE \(3.2\,<\,3.4\,<\,3.6\,\text{kcal}\,\text{mol}^{-1}\)—95 % confidence interval) for the whole data set and in particular for the octa-acid systems (MUE \(1.7\,<\,1.9\,<\,2.1\,\text{kcal}\,\text{mol}^{-1}\)). The overall correlation with experimental data for all models is encouraging (\(R^2\, 0.65\,<\,0.70<0.75\)). The correlation between experimental and computational free energy of binding ranks as one of the highest with respect to other entries in the challenge. Nonetheless the large MUE for the best performing model highlights systematic errors, and submissions from other groups fared better with respect to this metric.     

Interrogating HIV integrase for compounds that bind- a SAMPL challenge

Tremendous gains and novel methods are often developed when people are challenged to do something new or difficult. This process is enhanced when people compete against each other-this can be seen in sport as well as in science and technology (e.g. the space race). The SAMPL challenges, like the CASP challenges, aim to challenge modellers and software developers to develop new ways of looking at molecular interactions so the community as a whole can progress in the accurate prediction of these interactions. In order for this challenge to occur, data must be supplied so the prospective test can be done. We have supplied unpublished data related to a drug discovery program run several years ago on HIV integrase for the SAMPL4 challenge. This paper describes the methods used to obtain these data and the chemistry involved.     

Quantum chemical predictions of water–octanol partition coefficients applied to the SAMPL6 log P 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.     

Predicting octanol/water partition coefficients for the SAMPL6 challenge using the SM12, SM8, and SMD solvation models

Journal of Computer-Aided Molecular Design - Blind predictions of octanol/water partition coefficients at 298 K for 11 kinase inhibitor fragment like compounds were made for the SAMPL6 challenge....     

Computational fragment-based screening using RosettaLigand: the SAMPL3 challenge

SAMPL3 fragment based virtual screening challenge provides a valuable opportunity for researchers to test their programs, methods and screening protocols in a blind testing environment. We participated in SAMPL3 challenge and evaluated our virtual fragment screening protocol, which involves RosettaLigand as the core component by screening a 500 fragments Maybridge library against bovine pancreatic trypsin. Our study reaffirmed that the real test for any virtual screening approach would be in a blind testing environment. The analyses presented in this paper also showed that virtual screening performance can be improved, if a set of known active compounds is available and parameters and methods that yield better enrichment are selected. Our study also highlighted that to achieve accurate orientation and conformation of ligands within a binding site, selecting an appropriate method to calculate partial charges is important. Another finding is that using multiple receptor ensembles in docking does not always yield better enrichment than individual receptors. On the basis of our results and retrospective analyses from SAMPL3 fragment screening challenge we anticipate that chances of success in a fragment screening process could be increased significantly with careful selection of receptor structures, protein flexibility, sufficient conformational sampling within binding pocket and accurate assignment of ligand and protein partial charges.     

Predicting octanol/water partition coefficients and pKa for the SAMPL7 challenge using the SM12, SM8 and SMD solvation models

Journal of Computer-Aided Molecular Design - Blind predictions of octanol/water partition coefficients and pKa at 298.15 K for 22 drug-like compounds were made for the SAMPL7 challenge....     

The DINGO dataset: a comprehensive set of data for the SAMPL challenge

Part of the latest SAMPL challenge was to predict how a small fragment library of 500 commercially available compounds would bind to a protein target. In order to assess the modellers' work, a reasonably comprehensive set of data was collected using a number of techniques. These included surface plasmon resonance, isothermal titration calorimetry, protein crystallization and protein crystallography. Using these techniques we could determine the kinetics of fragment binding, the energy of binding, how this affects the ability of the target to crystallize, and when the fragment did bind, the pose or orientation of binding. Both the final data set and all of the raw images have been made available to the community for scrutiny and further work. This overview sets out to give the parameters of the experiments done and what might be done differently for future studies.