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.

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.

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.

The aza-Wittig reaction of iminophosphoranes (X3PdNH, X=H and Cl) with formaldehyde (H2CO) was investigated in gas phase and in water using ab initio MP2/6-31G** level of theory and the self-consistent reaction field theory (isodensity polarized continuum model, IPCM). In the gas phase, the aza-Wittig reaction was predicted to be a two-step process with two dipole-dipole complexes, one four-membered ring intermediate and two transition states. The potential energy profiles along the minima energy path in gas phase and in water were obtained. The solvent effects on the thermodynamic and kinetic properties of this reaction were discussed. This aza-Wittig reaction is more favorable for X=H than for X=Cl, both in the gas phase and in water

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.