GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research
| Autor: | Mathialakan Thavappiragasam, Oscar Hernandez, Josh V. Vermaas, Aaron Scheinberg, Ada Sedova, Andreas Koch, Duncan Poole, Jeremy C. Smith, Leonardo Solis-Vasquez, Stefano Forli, Andreas F. Tillack, Rupesh Agarwal, Scott LeGrand, Diogo Santos-Martins, Jeff Larkin |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
J.3
Quantitative Biology - Quantitative Methods 01 natural sciences Porting Article 03 medical and health sciences CUDA 0103 physical sciences D.1.3 Quantitative Methods (q-bio.QM) 030304 developmental biology 0303 health sciences geography Summit geography.geographical_feature_category Experimental drug 010304 chemical physics Drug discovery Biomolecules (q-bio.BM) Supercomputer ComputingMethodologies_PATTERNRECOGNITION Quantitative Biology - Biomolecules Computer architecture Protein–ligand docking Docking (molecular) FOS: Biological sciences |
| Zdroj: | ArXiv BCB |
| Popis: | Protein-ligand docking is an in silico tool used to screen potential drug compounds for their ability to bind to a given protein receptor within a drug-discovery campaign. Experimental drug screening is expensive and time consuming, and it is desirable to carry out large scale docking calculations in a high-throughput manner to narrow the experimental search space. Few of the existing computational docking tools were designed with high performance computing in mind. Therefore, optimizations to maximize use of high-performance computational resources available at leadership-class computing facilities enables these facilities to be leveraged for drug discovery. Here we present the porting, optimization, and validation of the AutoDock-GPU program for the Summit supercomputer, and its application to initial compound screening efforts to target proteins of the SARS-CoV-2 virus responsible for the current COVID-19 pandemic. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|