ABSTRACT
The molecular docking-based virtual screening technique evaluates the binding abilities between multiple ligand compounds and receptors to screen for the active compounds. In the context of the global spread of the COVID-19 pandemic, large-scale and rapid drug virtual screening is crucial for identifying potential drug molecules from massive datasets of ligand structures. The powerful computing power of supercomputer provides hardware guarantee for drug virtual screening, but the super large-scale drug virtual screening still faces many challenges that affects the effective execution of the calculation. Based on the analysis of the challenges, this paper proposes a centralized task distribution scheme with a central database, and designs a multi-level task distribution framework. The challenges are effectively solved through multi-level intelligent scheduling, multi-level compression processing of massive small molecule files, dynamic load balancing and high error tolerance management technology. An easy-touse"tree”multi-level task distribution system is implemented. A fast, efficient and stable drug virtual screening task distribution, calculation and result analysis function is realized, and the computing efficiency is nearly linear. Then, heterogeneous computing technology is used to complete the drug virtual screening of more than 2 billion compounds, for two different active sites for COVID-19, on the domestic super computing system, which provides a powerful computing guarantee for the super large-scale rapid virtual screening of explosive malignant infectious diseases. © 2023, Journal of Computer Engineering and Applications Beijing Co., Ltd.;Science Press. All rights reserved.
ABSTRACT
The coronavirus disease of 2019 (COVID-19) pandemic speaks to the need for drugs that not only are effective but also remain effective given the mutation rate of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To this end, we describe structural binding-site insights for facilitating COVID-19 drug design when targeting RNA-dependent RNA polymerase (RDRP), a common conserved component of RNA viruses. We combined an RDRP structure data set, including 384 RDRP PDB structures and all corresponding RDRP-ligand interaction fingerprints, thereby revealing the structural characteristics of the active sites for application to RDRP-targeted drug discovery. Specifically, we revealed the intrinsic ligand-binding modes and associated RDRP structural characteristics. Four types of binding modes with corresponding binding pockets were determined, suggesting two major subpockets available for drug discovery. We screened a drug data set of 7894 compounds against these binding pockets and presented the top-10 small molecules as a starting point in further exploring the prevention of virus replication. In summary, the binding characteristics determined here help rationalize RDRP-targeted drug discovery and provide insights into the specific binding mechanisms important for containing the SARS-CoV-2 virus.