Enabling Trade-offs Between Accuracy and Computational Cost: Adaptive Algorithms to Reduce Time to Clinical Insight

Jumana Dakka, Kristof Farkas-Pall, Vivek Balasubramanian, Matteo Turilli, Shunzhou Wan, David W Wright, Stefan Zasada, Peter V Coveney and Shantenu Jha

PaperAbstract. The efficacy of drug treatments depends on how tightly small molecules bind to their target proteins. Quantifying the strength of these interactions (the so called `binding affinity`) is a grand challenge of computational chemistry, surmounting which could revolutionize drug design and provide the platform for patient specific medicine. Recently, evidence from blind challenge predictions and retrospective validation studies has suggested that molecular dynamics (MD) can now achieve useful predictive accuracy (1 kcal/mol). This accuracy is sufficient to greatly accelerate hit to lead and lead optimization. To translate these advances in predictive accuracy so as to impact clinical and/or industrial decision making requires that binding free energy results must be turned around on reduced timescales without loss of accuracy. This demands advances in algorithms, scalable software systems, and intelligent and efficient utilization of supercomputing resources. This work is motivated by the real world problem of providing insight from drug candidate data on a time scale that is as short as possible. Specifically, we reproduce results from a collaborative project between UCL and GlaxoSmithKline to study a congeneric series of drug candidates binding to the BRD4 protein - inhibitors of which have shown promising preclinical efficacy in pathologies ranging from cancer to inflammation. We demonstrate the use of a framework called HTBAC, designed to support the aforementioned requirements of accurate and rapid drug binding affinity calculations. HTBAC facilitates the execution of the numbers of simulations while supporting the adaptive execution of algorithms. Furthermore, HTBAC enables the selection of simulation parameters during runtime which can, in principle, optimize the use of computational resources whilst producing results within a target uncertainty.