GPU-optimized approaches to molecular docking-based virtual screening in drug discovery: A comparative analysis
Emanuele Vitali, Federico Ficarelli, Mauro Bisson, Davide Gadioli, Gianmarco Accordi, Massimiliano Fatica, Andrea R. Beccari, Gianluca Palermo
Abstract
Finding a novel drug is a very long and complex procedure. Using computer simulations, it is possible to accelerate the preliminary phases by performing a virtual screening that filters a large set of drug candidates to a manageable number. This paper presents the implementations and comparative analysis of two GPU-optimised implementations of a virtual screening algorithm targeting novel GPU architectures. This work focuses on the analysis of parallel computation patterns and their mapping onto the target architecture. The first method adopts a traditional approach that spreads the computation for a single molecule across the entire GPU. The second uses a novel batched approach that exploits the parallel architecture of the GPU to evaluate more molecules in parallel. Experimental results showed a different behavior depending on the size of the database to be screened, either reaching a performance plateau sooner or having a more extended initial transient period to achieve a higher throughput (up to 5x), which is more suitable for extreme-scale virtual screening campaigns.