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Graphics processing unit computing, with its outstanding parallel computing power, is becoming increasingly important. There is a trade-off between cost effectiveness and accessibility in using on-demand computing resources rather than free/academic resources. Distributed computing resources can be difficult to access, although web-based solutions are becoming increasingly available.

The use of infrastructures relying on virtualization reduces set-up costs. Distributed computing environments have clear advantages over in-house solutions in terms of cost and sustainability.

HPC has a crucial role in handling the complex algorithms and large volumes of data required to achieve specificity and avoid undesirable side-effects.

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Supporting a local HPC infrastructure is both costly and time consuming and therefore many research groups are moving from in-house solutions to remote distributed computing platforms.Īreas covered: The authors focus on the use of distributed technologies, solutions and infrastructures to gain access to HPC capabilities, software tools and datasets to run the complex simulations required in computational drug discovery.Įxpert opinion: The use of computational tools can decrease the time to market of new drugs. Introduction: Computational chemistry dramatically accelerates the drug discovery process and high-performance computing (HPC) can be used to speed up the most expensive calculations. The first system recommends a receptor-ligand pair for the next docking experiment, and the second filters docking results based on ligand properties. Based on the framework and methodology, two prototype implementations of science-gateway-based docking environments are presented and evaluated. The methodology, which proposes three techniques that the development team can use, is agile and encourages active participation of end-users. The framework is modular enabling the reuse of already existing components. This paper describes a generic framework and a related methodology that supports the efficient development of such environments. While science gateways supporting such experiments are relatively common, there is a clearly identified need to design and implement more complex environments for further analysis of docking results. Molecular docking and virtual screening experiments require large computational and data resources and high-level user interfaces in the form of science gateways.

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It also shows that storing and sharing molecular docking results can produce additional conclusions such as viewing similar docking input files for verification or learning. The presented analysis illustrates that this approach of extending a domain-specific desktop application can use different types of clouds, thus facilitating the execution of virtual screening simulations by life scientists without requiring them to abandon their favourite desktop environment and providing them resources without major capital investment. A proof of concept is implemented using the Raccoon2 desktop application for virtual screening, WS-PGRADE workflows, gUSE services with the CloudBroker Platform, the structural alignment tool DeepAlign, and the ligand similarity tool LIGSIFT. A generic approach based on interviews with scientists and analysis of existing systems is proposed. This article investigates how domain-specific desktop applications can be extended to use cloud computing and how they can be part of scenarios that require sharing and analysing previous molecular docking results. This is why user-friendly domain-specific web or desktop applications that enable running simulations on powerful computing infrastructures have been created. Structure-based virtual screening simulations, which are often used in drug discovery, can be very computationally demanding.

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