Large-scale computing technologies have enabled high-throughput virtual screening involving thousands to millions of drug candidates. It is not trivial, however, for biochemical scientists to evaluate the technical alternatives and their implications for running such large experiments. Besides experience with the molecular docking tool itself, the scientist needs to learn how to run it on high-performance computing (HPC) infrastructures, and understand the impact of the choices made. Here, we review such considerations for a specific tool, AutoDock Vina, and use experimental data to illustrate the following points: (1) an additional level of parallelization increases virtual screening throughput on a multi-core machine; (2) capturing of the random seed is not enough (though necessary) for reproducibility on heterogeneous distributed computing systems; (3) the overall time spent on the screening of a ligand library can be improved by analysis of factors affecting execution time per ligand, including number of active torsions, heavy atoms and exhaustiveness. We also illustrate differences among four common HPC infrastructures: grid, Hadoop, small cluster and multi-core (virtual machine on the cloud). Our analysis shows that these platforms are suitable for screening experiments of different sizes. These considerations can guide scientists when choosing the best computing platform and set-up for their future large virtual screening experiments.Electronic supplementary materialThe online version of this article (doi:10.1007/s10822-016-9900-9) contains supplementary material, which is available to authorized users.

We present a modular method for schedulability analysis of real time distributed systems. We extend the actor model, as the asynchronous model for concurrent objects, with real time using timed automata, and show how actors can be analyzed individually to make sure that no task misses its deadline. We introduce drivers to specify how an actor can be safely used. Using these drivers we can verify schedulability, for a given scheduler, by doing a reachability check with the Uppaal model checker. Our method makes it possible to put a finite bound on the process queue and still obtain schedulability results that hold for any queue length.

Summary Science gateways provide UIs and high‐level services to access and manage applications and data collections on distributed resources. They facilitate users to perform data analysis on distributed computing infrastructures without getting involved into the technical details. The e‐BioInfra Gateway is a science gateway for biomedical data analysis on a national grid infrastructure, which has been successfully adopted for neuroscience research. This paper describes the motivation, requirements, and design of a new generation of e‐BioInfra Gateway, which is based on the grid and cloud user support environment (also known as WS‐PGRADE/gUSE framework) and supports heterogeneous infrastructures. The new gateway has been designed to have additional data and meta‐data management facilities to access and manage (biomedical) data servers, and to provide data‐centric user interaction. We have implemented and deployed the new gateway for the computational neuroscience research community of the Academic Medical Center of the University of Amsterdam. This paper presents the system architecture of the new gateway, highlights the improvements that have been achieved, discusses the choices that we have made, and reflects on those based on initial user feedback. Copyright © 2014 John Wiley & Sons, Ltd.