An empirical continuum solvation model, solvation free energy density (SFED), has been developed to calculate solvation free energies of a molecule in the most frequently used solvents. A generalized version of the SFED model, generalized-SFED (G-SFED), is proposed here to calculate molecular solvation free energies in virtually any solvent. G-SFED provides an accurate and fast generalized framework without a complicated description of a solution. In the model, the solvation free energy of a solute is represented as a linear combination of empirical functions of the solute properties representing the effects of solute on various solute-solvent interactions, and the complementary solvent effects on these interactions were reflected in the linear expansion coefficients with a few solvent properties. G-SFED works well for a wide range of sizes and polarities of solute molecules in various solvents as shown by a set of 5,753 solvation free energies of diverse combinations of 103 solvents and 890 solutes. Octanol-water partition coefficients of small organic compounds and peptides were calculated with G-SFED with accuracy within 0.4 log unit for each group. The G-SFED computation time depends linearly on the number of nonhydrogen atoms (n) in a molecule, O(n).implicit solvent | macromolecules | linear time A n accurate description of the effect of solvent on solvation is crucial for understanding chemical and biological phenomena. Because many peptide and protein drugs are currently being developed (1, 2), computational efficiency as well as accuracy are very important. Among the different approaches that have been used, continuum solvation models are appealing because of the simplified yet accurate description of the solvent effect (3-8).Continuum solvent models have been developed based on the early work of Born (9), Bell (10), Kirkwood (11), and Onsanger (12). They focus primarily on the description of the solute either at the quantum mechanical level or as a classical collection of point charges and try to represent the influence of the solvent based on an approximate treatment as a dielectric continuum medium. The charge distribution of the solute induces electric polarization of the surrounding solvent, and the electric field generated by the polarized solvent, the reaction field, in turn perturbs the solute, leading to a modification of the charge distribution of the solute. This electrostatic problem of the mutual polarization between solute and solvent can be recast by using boundary conditions at the cavity surface, leading to significant simplifications in the associated equations.In the classical approaches, most work is concerned with describing the electrostatic contribution to the solvation in terms of the Poisson-Boltzmann equation (PBE) (13). Several different computational techniques for solving the PBE have been developed, for example, finite difference methods in which the dielectric property of the solvent is described in terms of a 3D grid around the solute (14-17) and boundary element methods i...

The kidney is the most important organ for the excretion of pharmaceuticals and their metabolites. Among the complex structures of the kidney, the proximal tubule and renal interstitium are major targets of nephrotoxins. Despite its importance, there are only a few in silico models for predicting human nephrotoxicity for drug candidates. Here, we present quantitative structure-activity relationship (QSAR) models for three common patterns of drug-induced kidney injury, i.e., tubular necrosis, interstitial nephritis, and tubulo-interstitial nephritis. A support vector machine (SVM) was used to build the binary classification models of nephrotoxin versus non-nephrotoxin with eight fingerprint descriptors. To build the models, we constructed two types of data sets, i.e., parent compounds of pharmaceuticals (251 nephrotoxins and 387 non-nephrotoxins) and their major urinary metabolites (307 nephrotoxins and 233 non-nephrotoxins). Information on the nephrotoxicity of the pharmaceuticals was taken from clinical trial and postmarketing safety data. Though the mechanisms of nephrotoxicity are very complex, by using the metabolite information, the predictive accuracies of the best models for each type of kidney injury were better than 83% for external validation sets. Software to predict nephrotoxicity is freely available from our Web site at http://bmdrc.org/DemoDownload .

Since many drug development projects fail during clinical trials due to poor ADME properties, it is a wise practice to introduce ADME tests at the early stage of drug discovery. Various experimental and computational methods have been developed to obtain ADME properties in an economical manner in terms of time and cost. As in vitro and in vivo experimental data on ADME have accumulated, the accuracy of in silico models in ADME increases and thus, many in silico models are now widely used in drug discovery. Because of the demands from drug discovery researchers, the development of in silico models in ADME has become more active. In this chapter, the definitions of ADME endpoints are summarized, and in silico models related to ADME are introduced for each endpoint. Part I discusses the prediction models of the physicochemical properties of compounds, which influence much of the pharmacokinetics of pharmaceuticals. The prediction models of physical properties are developed based mainly on thermodynamics and are knowledge based, especially QSAR (quantitative structure activity relationship) methods. Part II covers the prediction models of the endpoints in ADME which include both in vitro and in vivo assay results. Most models are QSAR based and various kinds of descriptors (topology, 1D, 2D, and 3D descriptors) are used. Part III reviews physiologically based pharmacokinetic (PBPK) models.

Extractive distillation is a highly effective process for the separation of compound pairs having low relative volatility values, such as ethylbenzene (EB) and p-xylene (PX) mixtures. Many solvents or solvent mixtures have been screened experimentally to identify a suitable extraction agent for EB/PX mixtures. Because the number of possible solvent and solvent mixture candidates is high, it is necessary to introduce a computer-aided extraction performance prediction technique. In this study, a knowledge-based quantitative structure relative volatility relationship (QSRVR) model was developed using multiple linear regression (MLR) and artificial neural network (ANN) models, with each model having five descriptors. The root-meansquare errors (RMSE) of the training and test sets for the MLR model were calculated as 0.01486 and 0.00905, while their squared correlation coefficients (R 2 ) were 0.867 and 0.941, respectively. The R 2 and RMSE values of the total data set for the MLR model were 0.878 and 0.01408, and for the ANN model the values were 0.949 and 0.00929, respectively. The predictive ability of both models is sufficient for identifying suitable extractive distillation solvents for the separation of EB/PX mixtures.

The physics-based molecular force field (PMFF) was developed by integrating a set of potential energy functions in which each term in an intermolecular potential energy function is derived based on experimental values, such as the dipole moments, lattice energy, proton transfer energy, and X-ray crystal structures. The term “physics-based” is used to emphasize the idea that the experimental observables that are considered to be the most relevant to each term are used for the parameterization rather than parameterizing all observables together against the target value. PMFF uses MM3 intramolecular potential energy terms to describe intramolecular interactions and includes an implicit solvation model specifically developed for the PMFF. We evaluated the PMFF in three ways. We concluded that the PMFF provides reliable information based on the structure in a biological system and interprets the biological phenomena accurately by providing more accurate evidence of the biological phenomena.