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The drug discovery process involves several tasks to be performed in vivo, invitro and in silico. Molecular docking is a task typically performed in silico.It aims at finding the three-dimensional pose of a given molecule when itinteracts with the target protein binding site. This task is often done forvirtual screening a huge set of molecules to find the most promising ones,which will be forwarded to the later stages of the drug discovery process.Given the huge complexity of the problem, molecular docking cannot be solved byexploring the entire space of the ligand poses. State-of-the-art approachesface the problem by sampling the space of the ligand poses to generate resultsin a reasonable time budget. In this work, we improve the geometric approach tomolecular docking by introducing tunable approximations. In particular, weanalyzed and enriched the original implementation with tunable software knobsto explore and control the performance-accuracy tradeoffs. We modeledtime-to-solution of the virtual screening task as a function of software knobs,input data features, and available computational resources. Therefore, theapplication can autotune its configuration according to a user-defined timebudget. We used a Mini-App derived by LiGenDock - a state-of-the-art moleculardocking application - to validate the proposed approach. We run the enhancedMini-App on an HPC system by using a very large database of pockets andligands. The proposed approach exposes a time-to-solution interval spanningmore than one order of magnitude with accuracy degradation up to 30%, more ingeneral providing different accuracy levels according to the needs of thevirtual screening campaign.

Tunable Approximations to Control Time-to-Solution in an HPC Molecular Docking Mini-App