The cloud and other new computational methods to improve molecular modelling

Introduction: Industrial, as well as academic, drug discovery efforts are usually supported by computational modelling techniques. Many of these techniques, such as virtual high-throughput docking, pharmacophore-based screening of conformer databases and molecular dynamics simulations, are computationally very demanding. Depending on the parallelisation strategy applicable to the respective method, recent technologies based on central processing units, for example, cloud and grid computing, or graphics processing units (GPUs), can be employed to accelerate their execution times considerably. This allows the molecular modeller to look at larger data sets, or to use more accurate methods. Areas covered: The article introduces the recent developments in grid, cloud and GPU computing. The authors provide an overview of molecular modelling applications running on the above-mentioned hardware platforms and highlight caveats of the respective architectures, both from a theoretical and a practical point of view. Expert opinion: The architectures described can improve the molecular modelling process considerably, if the appropriate technologies are selected for the respective application. Despite these improvements, each of the individual computational platforms suffers from specific issues, which will need to be addressed in the future. Furthermore, current endeavours have focused on improving the performance of existing algorithms, rather than the development of new methods that explicitly harness these new technologies.