We present a new approach for predicting solvation free energies in nonaqueous solvents. Utilizing the corresponding states principle, we estimate solvent Lennard-Jones parameters directly from their critical points. Combined with atomic solutes and the pressure corrected three-dimensional reference interaction site model (3D-RISM/PC+), the model gives accurate predictions for a wide range of nonpolar solvents, including olive oil. The results, obtained without electrostatic interactions and with a very coarse-grained solvent, provide an interesting alternative to widely used and heavily parametrized models.

We present a theoretical/computational framework for accurate calculation of hydration free energies of ionized molecular species. The method is based on a molecular theory, 3D-RISM, combined with a recently developed pressure correction (PC+). The 3D-RISM/PC+ model can provide ∼3 kcal/mol hydration free energy accuracy for a large variety of ionic compounds, provided that the Galvani potential of water is taken into account. The results are compared with direct atomistic simulations. Several methodological aspects of hydration free energy calculations for charged species are discussed.

The aspartic protease, bovine chymosin, catalyzes the proteolysis of κ-casein proteins in milk. The bovine chymosin−κ-casein complex is of industrial interest as the enzyme is used extensively in the manufacturing of processed dairy products. The apo form of the enzyme adopts a self-inhibited conformation in which the side chain of Tyr77 occludes the binding site. On the basis of kinetic, mutagenesis, and crystallographic data, it has been widely reported that a HPHPH sequence in the P8–P4 residues of the natural substrate κ-casein acts as the allosteric activator, but the mechanism by which this occurs has not previously been elucidated due to the challenges associated with studying this process by experimental methods. Here we have employed two computational techniques, molecular dynamics and bias-exchange metadynamics simulations, to study the mechanism of allosteric activation and to compute the free energy surface for the process. The simulations reveal that allosteric activation is initiated by interactions between the HPHPH sequence of κ-casein and a small α-helical region of chymosin (residues 112–116). A small conformational change in the α-helix causes the side chain of Phe114 to vacate a pocket that may then be occupied by the side chain of Tyr77. The free energy surface for the self-inhibited to open transition is significantly altered by the presence of the HPHPH sequence of κ-casein.

We report a method to predict physicochemical properties of druglike molecules using a classical statistical mechanics based solvent model combined with machine learning. The RISM-MOL-INF method introduced here provides an accurate technique to characterize solvation and desolvation processes based on solute–solvent correlation functions computed by the 1D reference interaction site model of the integral equation theory of molecular liquids. These functions can be obtained in a matter of minutes for most small organic and druglike molecules using existing software (RISM-MOL) (Sergiievskyi, V. P.; Hackbusch, W.; Fedorov, M. V. J. Comput. Chem. 2011, 32, 1982–1992). Predictions of caco-2 cell permeability and hydration free energy obtained using the RISM-MOL-INF method are shown to be more accurate than the state-of-the-art tools for benchmark data sets. Due to the importance of solvation and desolvation effects in biological systems, it is anticipated that the RISM-MOL-INF approach will find many applications in biophysical and biomedical property prediction.

We report the results of testing quantitative structure–property relationships (QSPR) that were trained upon the same druglike molecules but two different sets of solubility data: (i) data extracted from several different sources from the published literature, for which the experimental uncertainty is estimated to be 0.6–0.7 log S units (referred to mol/L); (ii) data measured by a single accurate experimental method (CheqSol), for which experimental uncertainty is typically <0.05 log S units. Contrary to what might be expected, the models derived from the CheqSol experimental data are not more accurate than those derived from the “noisy” literature data. The results suggest that, at the present time, it is the deficiency of QSPR methods (algorithms and/or descriptor sets), and not, as is commonly quoted, the uncertainty in the experimental measurements, which is the limiting factor in accurately predicting aqueous solubility for pharmaceutical molecules.Keywords: ADME; ADMET; bioavailability; CheqSol; crystal; dissolution; druglike; experimental error; general solubility equation; Henderson−Hasselbalch; machine learning; Noyes−Whitney; pharmaceutical; polymorph; QSAR; QSPR; Random Forest; rule-of-five; solubility;

We demonstrate that the relative binding thermodynamics of single-point mutants of a model protein–peptide complex (the bovine chymosin–bovine κ-casein complex) can be calculated accurately and efficiently using molecular integral equation theory. The results are shown to be in good overall agreement with those obtained using implicit continuum solvation models. Unlike the implicit continuum models, however, molecular integral equation theory provides useful information about the distribution of solvent density. We find that experimentally observed water-binding sites on the surface of bovine chymosin can be identified quickly and accurately from the density distribution functions computed by molecular integral equation theory. The bovine chymosin–bovine κ-casein complex is of industrial interest because bovine chymosin is widely used to cleave bovine κ-casein and to initiate milk clotting in the manufacturing of processed dairy products. The results are interpreted in light of the recent discovery that camel chymosin is a more efficient clotting agent than bovine chymosin for bovine milk.