@article{VITALI2024104819,
  title    = {GPU-optimized approaches to molecular docking-based virtual screening in drug discovery: A comparative analysis},
  journal  = {Journal of Parallel and Distributed Computing},
  volume   = {186},
  pages    = {104819},
  year     = {2024},
  issn     = {0743-7315},
  doi      = {https://doi.org/10.1016/j.jpdc.2023.104819},
  url      = {https://www.sciencedirect.com/science/article/pii/S0743731523001892},
  author   = {Emanuele Vitali and Federico Ficarelli and Mauro Bisson and Davide Gadioli and Gianmarco Accordi and Massimiliano Fatica and Andrea R. Beccari and Gianluca Palermo},
  keywords = {High Performance Computing, High throughput virtual screening, GPU acceleration, CUDA, Batch},
  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-optimized 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.}
}