Harnessing quality-throughput trade-off in scoring functions for extreme-scale virtual screening campaigns

Drug discovery is a long and costly process aimed at finding a molecule that yields a therapeutic effect. Virtual screening is one of the initial in-silico steps that aims at estimating how promising a molecule is. This stage needs to solve two well-known domain problems: molecular docking and scoring. While the accuracy of scoring functions is extensively investigated in comparisons, the execution time of their implementation is usually not considered. In virtual screening campaigns, the definition of a fixed time budget for the entire process and the average time required to process each molecule determines the upper limit of the number of molecules that can be evaluated. By reducing the time needed to evaluate a single molecule, we can screen a larger number of molecules, thereby increasing the possibility of finding a promising solution. For extreme-scale virtual screening campaigns, the computational budget is a critical aspect since even utilizing large-scale facilities would make it impractical to complete the screening within a feasible time unless the computational time for a single molecule is significantly reduced.

In this paper, we explore optimization and approximation techniques applied to two well-known scoring functions, which we modify to investigate different accuracy-performance trade-offs to support large-scale virtual screening campaigns. Despite the different approaches we considered, experimental results demonstrate that the proposed enhancements achieve better enrichment factors in virtual screening scenarios. Moreover, we port both implementations to CUDA to show that the proposed techniques are GPU-friendly and aligned with modern supercomputing infrastructures.