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.

§ Longcheng Zhang and Jie Liang contributed equally to this work. Benzoate anions-intercalated NiFe-LDH nanosheet on carbon cloth (BZ-NiFe-LDH/CC) behaves as a highly efficient and durable electrocatalyst for alkaline seawater oxidation. In alkaline seawater, it attains the current density of 500 mA cm -2 at a low overpotential of 610 mV for 100-h uninterrupted electrolysis with no obvious structural change, reflecting significantly boosted activity and resistance toward chlorine species corrosion.

Recently, enols have been found to be the common intermediates in hydrocarbon combustion flames (Taatjes et al. Science 2005, 308, 1887), but the knowledge of kinetic properties for such species in combustion flames is rare. Therefore in this work, particular attention is paid to the formation of enols in combustion flames. Starting with HO and propene (CH(3)CH=CH(2)), the reaction mechanism involving eight product channels has been investigated systematically. It is revealed that the electrophilic addition of OH to the double bond of CH(3)CH=CH(2) is unselective and the chemically activated adducts, CH(3)CHOH=CH(2) and CH(3)CH=CH(2)OH, may undergo dissociation in competition with H-abstractions. The kinetics and product branching ratios of the HO and propene reaction have been evaluated in the temperature range of 200-3000 K by Variflex code, based on the weak collision master equation/microcanonical variational RRKM theory. Available experimental kinetic data can be quantitatively reproduced by this study, with a minor adjustment (1.0 kcal/mol) of the OH central addition barrier. From the theoretical calculations with multiple reflection correction included, the total rate constant is fitted to k(t) = 6.07 x 10(-5)T(-2.54) exp(108/T) cm(3) x molecule(-1) x s(-1) in the range of 200-800 K and k(t) = 7.11 x 10(-23)T(3.38) exp(-1097/T) cm(3) x molecule(-1) x s(-1) in the range of 800-3000 K, which are in close agreement with experimental data. The branching ratios of enol channels are consistent with the observation in low-pressure flames and hence the reaction mechanisms presented here provide valuable descriptions of enol formations in hydrocarbon combustion chemistry.

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