Application of grid computing to parameter sweeps and optimizations in molecular modeling

In science and engineering in general and in computational chemistry in particular, parameter sweeps and optimizations are of high importance. Such parametric modeling jobs are embarrassingly parallel and thus well suited for grid computing. The Nimrod toolkit significantly simplifies the utilization of computational grids for this kind of research by hiding the complex grid middleware, automating job distribution, and providing easy-to-use user interfaces. Here, we present examples for the usage of Nimrod in molecular modeling. In detail, we discuss the parameterization of a group difference pseudopotential (GDP). Other applications are protein–ligand docking and a high-throughput workflow infrastructure for computational chemistry.