Ruthenium(III)-catalyzed oxidation of amides by sodium N-bromobenzenesulphonamide in hydrochloric acid: a kinetic and mechanistic study

The kinetics of the RuIII-catalyzed oxidation of urea and substituted ureas namely: methylurea, ethylurea and propylurea, to the corresponding hydrazines by sodium N- bromobenzenesulphonamide or bromamine-B (BAB) in HCl medium has been studied at 303K. The reaction rate shows a first order dependence each upon [BAB], [amide] and [RuIII] and is dependent on [H+]. Addition of halide ions and benzenesulphonamide do not significantly affect the rate. Proton inventory studies were made in H2O-D2O mixtures for urea and methylurea. A Taft linear free energy relationship is observed for the reaction with *=–2.95 and =–0.25, showing that electron-donating groups enhance the rate. An isokinetic relation is observed with =370K, indicating that enthalpy factors control the rate; this result is also confirmed by Exner Criterion which showed a linear plot for the logarithms of rate constants at the highest and lowest temperatures employed. The protonation constant of monobromamine-B has been evaluated and equals 7.5. A mechanism consistent with the observed kinetic data has been proposed.     

Antimony(III) Sulfate Catalyzed Condensation Reaction of Indoles with Carbonyl Compounds

Summary. Antimony sulfate was found to be on effective catalyst for the condensation reaction of indoles with carbonyl compounds at room temperature. This catalyst is inexpensive, easily available and it was also found that this catalyst could be recovered quantitatively and reused without much loss of catalytic activity.     

Active learning with support vector machines in the drug discovery process

We investigate the following data mining problem from computer-aided drug design: From a large collection of compounds, find those that bind to a target molecule in as few iterations of biochemical testing as possible. In each iteration a comparatively small batch of compounds is screened for binding activity toward this target. We employed the so-called "active learning paradigm" from Machine Learning for selecting the successive batches. Our main selection strategy is based on the maximum margin hyperplane-generated by "Support Vector Machines". This hyperplane separates the current set of active from the inactive compounds and has the largest possible distance from any labeled compound. We perform a thorough comparative study of various other selection strategies on data sets provided by DuPont Pharmaceuticals and show that the strategies based on the maximum margin hyperplane clearly outperform the simpler ones.