Ab initio molecular orbital method and Monte Carlo (MC) simulation with free energy perturbation (FEP) techniques have been used to study the aza-Wittig reaction of iminophosphoranes (H3PdNH) with formaldehyde (H2CO) in the gas phase and in three different solvents: water, methanol, and tetrahydrofuran (THF). The optimized structures and thermodynamic properties of stationary points for the title reaction system in the gas phase were calculated at the MP2/6-31G** level of theory. The effects of substituents on the reactivity of iminophosphorane were discussed. This aza-Wittig reaction is more favorable for XdH and CH3 than for XdCl in the gas phase. The potential energy profiles along the minimum energy path in the gas phase and in three solvents were obtained. The solvent effects on the H3PdNH + H2CO reaction increase in the order water ≈ methanol>THF, suggesting that the protic polar solvents are more suitable for the aza-Wittig reaction.

Statistical-learning methods have been developed for facilitating the prediction of pharmacokinetic and toxicological properties of chemical agents. These methods employ a variety of molecular descriptors to characterize structural and physicochemical properties of molecules. Some of these descriptors are specifically designed for the study of a particular type of properties or agents, and their use for other properties or agents might generate noise and affect the prediction accuracy of a statistical learning system. This work examines to what extent the reduction of this noise can improve the prediction accuracy of a statistical learning system. A feature selection method, recursive feature elimination (RFE), is used to automatically select molecular descriptors for support vector machines (SVM) prediction of P-glycoprotein substrates (P-gp), human intestinal absorption of molecules (HIA), and agents that cause torsades de pointes (TdP), a rare but serious side effect. RFE significantly reduces the number of descriptors for each of these properties thereby increasing the computational speed for their classification. The SVM prediction accuracies of P-gp and HIA are substantially increased and that of TdP remains unchanged by RFE. These prediction accuracies are comparable to those of earlier studies derived from a selective set of descriptors. Our study suggests that molecular feature selection is useful for improving the speed and, in some cases, the accuracy of statistical learning methods for the prediction of pharmacokinetic and toxicological properties of chemical agents.

Density functional theory (DFT) and Monte Carlo (MC) simulation with free energy perturbation (FEP) techniques have been used to study the tautomeric proton transfer reaction of 2-amino-2-oxazoline, 2-amino-2-thiazoline, and 2-amino-2-imidazoline in the gas phase and in water. Two reaction pathways were considered: the direct and water-assisted transfers. The optimized structures and thermodynamic properties of stationary points for the title reaction system in the gas phase were calculated at the B3LYP/6-311 G(d, p) level of theory. The potential energy profiles along the minimum energy path in the gas phase and in water were obtained. The study of the solvent effect of water on the proton transfer of 2-amino-2-oxozoline, 2-amino-2-thiazoline, and 2-amino-2-imidazoline indicates that water as a solvent is favorable for the water-assisted process and slows down the rate of the direct transfer pathway.

Toxicity of various compounds has been measured in many studies by their toxic effects against Tetrahymena pyriformis. Efforts have also been made to use computational quantitative structure-activity relationship (QSAR) and statistical learning methods (SLMs) for predicting Tetrahymena pyriformis toxicity (TPT) at impressive accuracies. Because of the diversity of compounds and toxicity mechanisms, it is desirable to explore additional methods and to examine if these methods are applicable to more diverse sets of compounds. We tested several SLMs (logistic regression, C4.5 decision tree, k-nearest neighbor, probabilistic neural network, support vector machines) for their capability in predicting TPT by using 1129 compounds (841 TPT and 288 non-TPT agents) which are more diverse than those in other studies. A feature selection method was used for improving prediction performance and selecting molecular descriptors responsible for distinguishing TPT and non-TPT agents. The prediction accuracies are 86.9% 94.2% for TPT and 71.2% 87.5% for non-TPT agents based on 5-fold cross-validation studies, which are comparable to some of earlier studies despite the use of more diverse sets of compounds. The selected molecular descriptors are consistent with those used in other studies and experimental findings. These suggest that SLMs are useful for predicting TPT potential of diverse sets of compounds and for characterizing the molecular descriptors associated with TPT.

The chemical fixation of nitric oxide (NO) reacting with alkynyllithium to produce 5-methyl-3-oxide-1,2,3-oxadiazole has been investigated by using ab initio (U)MP2 and DFT/(U)B3LYP methods. The solvent effect was assessed using the combination of microsolvation model with explicit THF ligands on lithium and continuum solvent model based on the SCRF/CPCM method at the (U)-B3LYP/6-31G* level. Our results reveal that the overall reaction is stepwise and considered to include two processes. In process 1, the nitrogen atom in nitric oxide at first attacks the C1 atom in alkynyllithium to afford the intermediate 5. In process 2, after another nitric oxide reacted with the intermediate 5 to produce 8a, we found that two pathways are involved. For path 1, the O2 atom at first attacks the C2 atom to form a five-membered ring geometry, and then lithium can rotate around the N1-O1 bond, leading to the product 5-methyl-3-oxide-1,2,3-oxadiazole followed addition of water. However, for path 2, lithium atom rotates first around the N1-O1 bond, and then the product 5-methyl-3-oxide-1,2,3-oxadiazole is also generated by addition of water. Our calculations indicate that path 1 is more favorable than path 2 in the gas phase, while both of them exist possibly in THF solvent. The overall reaction is exothermic.