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.

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.

P-glycoproteins (P-gp) actively transport a wide variety of chemicals out of cells and function as drug efflux pumps that mediate multidrug resistance and limit the efficacy of many drugs. Methods for facilitatingearly elimination of potential P-gp substrates are useful for facilitating new drug discovery. A computational ensemble pharmacophore model has recently been used for the prediction of P-gp substrates with a promising accuracy of 63%. It is desirable to extend the prediction range beyond compounds covered by the known pharmacophore models. For such a purpose, a machine learning method, support vector machine (SVM), was explored for the prediction of P-gp substrates. A set of 201 chemical compounds, including 116 substrates and 85 nonsubstrates of P-gp, was used to train and test a SVM classification system. This SVM system gave a prediction accuracy of at least 81.2% for P-gp substrates based on two different evaluation methods, which is substantially improved against that obtained from the multiple-pharmacophore model. The prediction accuracy for nonsubstrates of P-gp is 79.2% using 5-fold cross-validation. These accuracies are slightly better than those obtained from other statistical classification methods, including k-nearest neighbor (k-NN), probabilistic neural networks (PNN), and C4.5 decision tree, that use the same sets of data and molecular descriptors. Our study indicates the potential of SVM in facilitating the prediction of P-gp substrates.

In an effort to facilitate drug discovery, computational methods for facilitating the prediction of various adverse drug reactions (ADRs) have been developed. So far, attention has not been sufficiently paid to the development of methods for the prediction of serious ADRs that occur less frequently. Some of these ADRs, such as torsade de pointes (TdP), are important issues in the approval of drugs for certain diseases. Thus there is a need to develop tools for facilitating the prediction of these ADRs. This work explores the use of a statistical learning method, support vector machine (SVM), for TdP prediction. TdP involves multiple mechanisms and SVM is a method suitable for such a problem. Our SVM classification system used a set of linear solvation energy relationship (LSER) descriptors and was optimized by leave-oneout cross validation procedure. Its prediction accuracy was evaluated by using an independent set of agents and by comparison with results obtained from other commonly used classification methods using the same dataset and optimization procedure. The accuracies for the SVM prediction of TdP-causing agents and non-TdP-causing agents are 97.4 and 84.6% respectively; one is substantially improved against and the other is comparable to the results obtained by other classification methods useful for multiple-mechanism prediction problems. This indicates the potential of SVM in facilitating the prediction of TdP-causing risk of small molecules and perhaps other ADRs that involve multiple mechanisms.