Fast calculation of thermodynamic and structural parameters of solutions using the 3DRISM model and the multi-grid method

In the paper a new method to solve the tree-dimensional reference interaction site model (3DRISM) integral equations is proposed. The algorithm uses the multi-grid technique which allows to decrease the computational expanses. 3DRISM calculations for aqueous solutions of four compounds (argon, water, methane, methanol) on the different grids are performed in order to determine a dependence of the computational error on the parameters of the grid. It is shown that calculations on the grid with the step 0.05\Angstr and buffer 8\Angstr give the error of solvation free energy calculations less than 0.3 kcal/mol which is comparable to the accuracy of the experimental measurements. The performance of the algorithm is tested. It is shown that the proposed algorithm is in average more than 12 times faster than the standard Picard direct iteration method.Comment: the information in this preprint is not up to date. Since the first publication of the preprint (9 Nov 2011) the algorithm was modified which allowed to achieve better results. For the new algorithm see the JCTC paper: DOI: 10.1021/ct200815v, http://pubs.acs.org/doi/abs/10.1021/ct200815

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xviii, 518 p.; 23 cm

Uniting cheminformatics and chemical theory to predict the intrinsic aqueous solubility of crystalline druglike molecules

We present four models of solution free-energy prediction for druglike molecules utilizing cheminformatics descriptors and theoretically calculated thermodynamic values. We make predictions of solution free energy using physics-based theory alone and using machine learning/quantitative structure–property relationship (QSPR) models. We also develop machine learning models where the theoretical energies and cheminformatics descriptors are used as combined input. These models are used to predict solvation free energy. While direct theoretical calculation does not give accurate results in this approach, machine learning is able to give predictions with a root mean squared error (RMSE) of ~1.1 log S units in a 10-fold cross-validation for our Drug-Like-Solubility-100 (DLS-100) dataset of 100 druglike molecules. We find that a model built using energy terms from our theoretical methodology as descriptors is marginally less predictive than one built on Chemistry Development Kit (CDK) descriptors. Combining both sets of descriptors allows a further but very modest improvement in the predictions. However, in some cases, this is a statistically significant enhancement. These results suggest that there is little complementarity between the chemical information provided by these two sets of descriptors, despite their different sources and methods of calculation. Our machine learning models are also able to predict the well-known Solubility Challenge dataset with an RMSE value of 0.9–1.0 log S units.Publisher PDFPeer reviewe

Toward a universal model to calculate the solvation thermodynamics of druglike molecules : the importance of new experimental databases

We demonstrate that a new free energy functional in the integral equation theory of molecular liquids gives accurate calculations of hydration thermodynamics for drug-like molecules. The functional provides an improved description of excluded volume effects by incorporating two free coefficients. When the values of these coefficients are obtained from experimental data for simple organic molecules, the hydration free energies of an external test set of druglike molecules can be calculated with an accuracy of about 1 kcal/mol. The 3D RISM/UC method proposed here is easily implemented using existing computational software and allows in silico screening of the solvation thermodynamics of potential pharmaceutical molecules at significantly lower computational expense than explicit solvent simulations

Combination of RISM and cheminformatics for efficient predictions of hydration free energy of polyfragment molecules : application to a set of organic pollutants

Here, we discuss a new method for predicting the hydration free energy (HFE) of organic pollutants and illustrate the efficiency of the method on a set of 220 chlorinated aromatic hydrocarbons. The new model is computationally inexpensive, with one HFE calculation taking less than a minute on a PC. The method is based on a combination of a molecular integral equations theory, one-dimensional reference interaction site model (1D RISM), with the cheminformatics approach. We correct HFEs obtained by the ID RISM with a set of empirical corrections. The corrections are associated with the partial molar volume and structural descriptors of the molecules. We show that the introduced corrections can significantly improve the quality of the ID RISM FIFE predictions obtained by the partial wave free energy expression [Ten-no, S. J. Chem. Phys. 2001, 115, 3724] and the Kovalenko-Hirata closure [Kovalenko, A.; Hirata, F. J. Chem. Phys. 1999, 110, 10095]. We also show that the quality of the model can be further improved by the reparametrization using QM-derived partial charges instead of the originally used OPLS-AA partial charges. The final model gives good results for polychlorinated benzenes (the mean and standard deviation of the error are 0.02 and 0.36 kcal/mol, correspondingly). At the same time, the model gives somewhat worse results for polychlorobiphenyls (PCBs) with a systematic bias of -0.72 kcal/mol but a small standard deviation equal to 0.55 kcal/mol. We note that the error remains the same for the whole set of PCBs, whereas errors of HFEs predicted with continuum solvation models (data were taken from Phillips, K L. et al. Environ. Sci. Technol. 2008, 42, 8412) increase significantly for higher chlorinated PCB congeners. In conclusion, we discuss potential future applications of the model and several avenues for its further improvement