COSMOsim: bioisosteric similarity based on COSMO-RS sigma profiles.

A novel approach for the quantification of drug similarity is proposed, which makes use of the surface polarities, that is, conductor surface polarization charge densities sigma, as defined in the quantum chemically based conductor-like screening model for realistic solvation(COSMO-RS). The histogram of these surface polarities, the so-called sigma profiles, have been proven to be the key for the calculation of all kinds of partition and adsorption coefficients and, therefore, of relevant absorption, distribution, metabolism, and excretion parameters such as solubility, pKa, log BB, and many others. They also carry a large part of the information required for the estimation of desolvation and binding processes responsible for receptor binding and enzyme inhibition of drug molecules. Thus, a large degree of similarity with respect to the sigma profiles appears to be a necessary condition for drugs of similar physiological action. Driven by this insight, we propose a sigma-profile-based drug similarity measure COSMOsim for the detection of new bioisosteric drug candidates. In several examples, we demonstrate its statistical and pharmaceutical plausibility, its practicability for real drug research projects, and its unique independence from the chemical structure, which enables scaffold hopping in a natural way.

COSMOfrag: a novel tool for high-throughput ADME property prediction and similarity screening based on quantum chemistry.

The COSMO-RS (Continuum Solvation Model for Real Solvents) method has proven its broad applicability for the accurate prediction of thermodynamic, environmental, or physiological properties. On the basis of quantum chemical calculations with COSMO, COSMO-RS calculations were unavoidably restricted to small- to medium-sized compound sets, because of the time demand of the COSMO calculations. The COSMOfrag method, presented here, overcomes this restriction by replacing the costly quantum chemistry step with a selection of suitable fragments from a database of, presently, 40,000 DFT/COSMO precalculated molecules. Since, in the COSMO-RS picture, any molecular information is gathered in the so-called sigma profiles, COSMOfrag replaces the single sigma profile with a composition of partial sigma profiles, selected by the use of extensive similarity searching algorithms. On five representative datasets, the accuracy loss of COSMOfrag versus full COSMO-RS calculations has been shown to be only in the range of 0.05 log units. From the performance point of view, it is now possible to carry out COSMO-RS property calculations for more than 100,000 compounds a day per standard PC CPU.

Prediction of aqueous solubility of drugs and pesticides with COSMO-RS.

The COSMO-RS method, originally developed for the prediction of liquid-liquid and liquid-vapor equilibrium constants based on quantum chemical calculations, has been extended to solid compounds by addition of a heuristic expression for the Gibbs free energy of fusion. By this addition, COSMO-RS is now capable of a priori prediction of aqueous solubilities of a wide range of typical neutral drug and pesticide compounds. Only three parameters in the heuristic expression have been fitted on a data set of 150 drug-like compounds. On these data an rms deviation of 0.66 log-units was achieved. Later, the model was tested on a set of 107 pesticides, which have been critically selected based on two experimental data sources and by a crosscheck with an independent HQSAR model. On this data set an rms of 0.61 log-units was achieved, without any adjustments to the structurally extremely diverse pesticides. This result verifies the ability of this extended COSMO-RS to predict aqueous solubilities of drugs and pesticides of almost arbitrary structural classes. The new method is COSMO-RSol.