An attractive alternative to absolute calculations is thus the calculation of the difference in binding affinity between two systems (known as the relative binding free energy). In order for simulations to have an impact in industrial or clinical decision making processes calculations must not only be accurate, precise and reliable but also complete in a short period of time. The large changes which need to be accounted for in calculating absolute binding free energies (the ‘appearance’ or ‘disappearance’ of a ligand) are often slow to converge and consequently extremely computationally expensive. They employ molecular simulations-molecular dynamics (MD) or Monte Carlo (MC)-of unphysical, alchemical intermediate states that attenuate the interactions of the small molecule with its environment. One of the most popular and accurate approaches is alchemical methods. A wide range of techniques are available to compute binding affinities (also known as binding free energies) from atomistic simulation.

The accurate prediction of the binding affinities of ligands to proteins is a major goal in drug discovery and personalized medicine. In this paper, we focus on the software infrastructure and interfaces that we have developed to automate the overall workflow and execute it on commodity cloud platforms, in order to reliably predict their binding affinities on time scales relevant to the domains of application, and illustrate its application to two free energy methods. To automate the process, we have developed a workflow known as the binding affinity calculator. Free energies are determined by ensemble averages of many MD replicas, each of which requires hundreds of cores and/or GPU accelerators, which are now available on commodity cloud computing platforms there are also requirements for initial model building and subsequent data analysis stages. This approach is based on molecular dynamics (MD) simulations and draws on sequence and structural information of the protein and compound concerned. This is imperative in drug discovery as well as personalized medicine. In recent years, it has become possible to calculate binding affinities of compounds bound to proteins via rapid, accurate, precise and reproducible free energy calculations.

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