Electronic and structural properties of energetically low-lying isomers of isolated Ti x O y (x = 1-6, y = 1-12) molecular systems have been investigated by density functional theoretical methods. A variety of stationary points are thoroughly characterized. We report total cluster energies, equilibrium geometries and harmonic vibrational wavenumbers.

A convenient synthesis of disodium S-[4-(4-ethoxybenzyl)-3,6,9-tris[(carboxy-kappaO)methyl]-3,6,9-triazaundecandioato)(5-)-kappa(3)N(3)(),N(6)(),N(9)(),kappa(2)O(1)(),O(11)()]gadolinate(2-) (Gd-EOB-DTPA), 1, is reported. This water-soluble complex is presently undergoing phase III clinical trials as a liver-specific contrast agent for magnetic resonance imaging (MRI). The thermodynamic complex stability constant of 1 and the acid dissociation constants of the ligand have been determined as well as the stability constant of the calcium complex Ca-EOB-DTPA (2), which is used as an additive in the pharmaceutical formulation of the contrast agent. The solid-state structure of the ligand S-4-(4-ethoxybenzyl)-3,6,9-tris(carboxylatomethyl)-3,6,9-triazaundecanedioic acid (H(5)EOB-DTPA), 3, has been elucidated in a single crystal X-ray diffraction study. Additionally, HPLC evidence is given that the enantiomerically pure ligand forms two diastereomeric gadolinium complexes in a 65:35 ratio. The kinetics of isomerization of the isolated diastereomers-as dependent on pH and temperature-has been investigated, and thus, the activation energy for the interconversion of these isomers has been estimated to be 75.3 kJ mol(-1). Finally, the structures of the two components of 1 are discussed in terms of four possible diastereomers.

Accurate in silico models for predicting aqueous solubility are needed in drug design and discovery and many other areas of chemical research. We present a statistical modeling of aqueous solubility based on measured data, using a Gaussian Process nonlinear regression model (GPsol). We compare our results with those of 14 scientific studies and 6 commercial tools. This shows that the developed model achieves much higher accuracy than available commercial tools for the prediction of solubility of electrolytes. On top of the high accuracy, the proposed machine learning model also provides error bars for each individual prediction.

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