  The efficacy of drug treatments depends on how tightly small molecules bind
to their target proteins. The rapid and accurate quantification of the strength
of these interactions (as measured by binding affinity) is a grand challenge of
computational chemistry, surmounting which could revolutionize drug design and
provide the platform for patient-specific medicine. Recent evidence suggests
that molecular dynamics (MD) can achieve useful predictive accuracy (< 1
kcal/mol). For this predictive accuracy to impact clinical decision making,
binding free energy computational campaigns must provide results rapidly and
without loss of accuracy. This demands advances in algorithms, scalable
software systems, and efficient utilization of supercomputing resources. We
introduce a framework called HTBAC, designed to support accurate and scalable
drug binding affinity calculations, while marshaling large simulation
campaigns. We show that HTBAC supports the specification and execution of
free-energy protocols at scale. This paper makes three main contributions: (1)
shows the importance of adaptive execution for ensemble-based free energy
protocols to improve binding affinity accuracy; (2) presents and characterizes
HTBAC -- a software system that enables the scalable and adaptive execution of
binding affinity protocols at scale; and (3) for a widely used free-energy
protocol (TIES), shows improvements in the accuracy of simulations for a fixed
amount of resource, or reduced resource consumption for a fixed accuracy as a
consequence of adaptive execution.
