COSMO-RS predictions of logP in the SAMPL7 blind challenge.

We applied the COSMO-RS method to predict the partition coefficient logP between water and 1-octanol for 22 small drug like molecules within the framework of the SAMPL7 blind challenge. We carefully collected a set of thermodynamically meaningful microstates, including tautomeric forms of the neutral species, and calculated the logP using the current COSMOtherm implementation on the most accurate level. With this approach, COSMO-RS was ranked as the 6st most accurate method (Measured by the mean absolute error (MAE) of 0.57) over all 17 ranked submissions. We achieved a root mean square deviation (RMSD) of 0.78. The largest deviations from experimental values are exhibited by five SAMPL molecules (SM), which seem to be shifted in most SAMPL7 contributions. In context with previous SAMPL challenges, COSMO-RS demonstrates a wide range of applicability and one of the best in class reliability and accuracy among the physical methods.

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.

COSMOsim3D: 3D-similarity and alignment based on COSMO polarization charge densities.

COSMO σ-surfaces resulting from quantum chemical calculations of molecules in a simulated conductor, and their histograms, the so-called σ-profiles, are widely proven to provide a very suitable and almost complete basis for the description of molecular interactions in condensed systems. The COSMOsim method therefore introduced a global measure of molecular similarity on the basis of similarity of σ-profiles, but it had the disadvantage of neglecting the 3D distribution of molecular polarities, which is crucially determining all ligand-receptor binding. This disadvantage is now overcome by COSMOsim3D, which is a logical and physically sound extension of the COSMOsim method, which uses local σ-profiles on a spatial grid. This new method is used to measure intermolecular similarities on the basis of the 3D representation of the surface polarization charge densities σ of the target and the probe molecule. The probe molecule is translated and rotated in space in order to maximize the sum of local σ-profile similarities between target and probe. This sum, the COSMOsim3D similarity, is a powerful descriptor of ligand similarity and allows for a good discrimination between bioisosters and random pairs. Validation experiments using about 600 pharmacological activity classes in the MDDR database are given. Furthermore, COSMOsim3D represents a unique and very robust method for a field-based ligand-ligand alignment.

COSMOsar3D: molecular field analysis based on local COSMO σ-profiles.

The COSMO surface polarization charge density σ resulting from quantum chemical calculations combined with a virtual conductor embedding has been widely proven to be a very suitable descriptor for the quantification of interactions of molecules in liquids. In a preceding paper, grid-based local histograms of σ have been introduced in the COSMOsim3D method, resulting in a novel 3D-molecular similarity measure and going along with a novel property-based molecular alignment method. In this paper, we introduce under the name COSMOsar3D the usage of the resulting array of local σ-profiles as a novel set of molecular interaction fields for 3D-QSAR, containing all information required for quantifying the virtual ligand-receptor interactions, including desolvation. In contrast to currently used molecular interaction fields, we provide a theoretical rationale that the logarithmic binding constants of ligands should be a linear function of the array of local σ-profiles. This makes them especially suitable for linear regression analysis methods such as PLS. We demonstrate that the usage of local σ-profiles in molecular field analysis inverts the role of ligands and receptor; while conventional 3D-QSAR considers the virtual receptor in potential energy fields provided by the ligands, our COSMOsar3D approach corresponds to the calculation of the free energy of the ligands in a virtual free energy field provided by the receptor. First applications of the COSMOsar3D method are presented, which demonstrate its ability to yield robust and predictive models that seem to be superior to the models generated on the basis of conventionally used molecular fields.

A semiempirical approach to ligand-binding affinities: dependence on the Hamiltonian and corrections.

We present a combination of semiempirical quantum-mechanical (SQM) calculations in the conductor-like screening model with the MM/GBSA (molecular-mechanics with generalized Born and surface-area solvation) method for ligand-binding affinity calculations. We test three SQM Hamiltonians, AM1, RM1, and PM6, as well as hydrogen-bond corrections and two different dispersion corrections. As test cases, we use the binding of seven biotin analogues to avidin, nine inhibitors to factor Xa, and nine phenol-derivatives to ferritin. The results vary somewhat for the three test cases, but a dispersion correction is mandatory to reproduce experimental estimates. On average, AM1 with the DH2 hydrogen-bond and dispersion corrections gives the best results, which are similar to those of standard MM/GBSA calculations for the same systems. The total time consumption is only 1.3-1.6 times larger than for MM/GBSA.

Prediction of blood-brain partitioning and human serum albumin binding based on COSMO-RS sigma-moments.

Models for the prediction of blood-brain partitioning (logBB) and human serum albumin binding (logK(HSA)) of neutral molecules were developed using the set of 5 COSMO-RS sigma-moments as descriptors. These sigma-moments have already been introduced earlier as a general descriptor set for partition coefficients. They are obtained from quantum chemical calculations using the continuum solvation model COSMO and a subsequent statistical decomposition of the resulting polarization charge densities. The model for blood-brain partitioning was built on a data set of 103 compounds and yielded a correlation coefficient of r2 = 0.71 and an rms error of 0.40 log units. The human serum albumin binding model was built on a data set of 92 compounds and achieved an r2 of 0.67 and an rms error of 0.33 log units. Both models were validated by leave-one-out cross-validation tests, which resulted in q2 = 0.68 and a qms error of 0.42 for the logBB model and in q2 = 0.63 and a qms error of 0.35 for the logK(HSA) model. Together with the previously published models for intestinal absorption and for drug solubility the presented two models complete the COSMO-RS based set of ADME prediction models.