Photogeneration of Hydrogen from Water by Hybrid Molybdenum Sulfide Clusters Immobilized on Titania

Two new hybrid molybdenum(IV) Mo3S7 cluster complexes derivatized with diimino ligands have been prepared by replacement of the two bromine atoms of [Mo3S7Br6]2− by a substituted bipyridine ligand to afford heteroleptic molybdenum(IV) Mo3S7Br4(diimino) complexes. Adsorption of the Mo3S7 cores from sample solutions on TiO2 was only achieved from the diimino functionalized clusters. The adsorbed Mo3S7 units were reduced on the TiO2 surface to generate an electrocatalyst that reduces the overpotential for the H2 evolution reaction by approximately 0.3 V (for 1 mA cm−2) with a turnover frequency as high as 1.4 s−1. The nature of the actual active molybdenum sulfide species has been investigated by X-ray photoelectron spectroscopy. In agreement with the electrochemical results, the modified TiO2 nanoparticles show a high photocatalytic activity for H2 production in the presence of Na2S/Na2SO3 as a sacrificial electron donor system.

Improving drug discovery using hybrid softcomputing methods

Virtual screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface in order to find new hotspots, where ligands might potentially interact with, and which is implemented in last generation massively parallel GPU hardware, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods and concretely BINDSURF is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of the scoring functions used in BINDSURF we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, being this information exploited afterwards to improve BINDSURF VS predictions.