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Accelerating virtual high-throughput ligand docking: current technology and case study on a petascale supercomputer.

, , , , and . Concurr. Comput. Pract. Exp., 26 (6): 1268-1277 (2014)

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Computationally characterizing genomic pipelines using high-confident call sets., and . ICCS, volume 80 of Procedia Computer Science, page 1023-1032. Elsevier, (2016)VinaMPI: Facilitating multiple receptor high-throughput virtual docking on high-performance computers., , and . J. Comput. Chem., 34 (25): 2212-2221 (2013)Quantifying Overfitting Potential in Drug Binding Datasets., , , and . ICCS (3), volume 12139 of Lecture Notes in Computer Science, page 585-598. Springer, (2020)Using large public data repositories to discover novel genetic mutations with prospective links to melanoma., , , , , and . BMC Bioinform., 16 (S15): P3 (December 2015)Gene-disease-drug link prediction using tripartite graphs., , , and . BCB, page 76:1. ACM, (2021)Proceedings of the 15th Annual UT-KBRIN Bioinformatics Summit 2016: Cadiz, KY, USA. 8-10 April 2016., , , , , , , , , and 70 other author(s). BMC Bioinform., (August 2016)High-throughput virtual molecular docking with AutoDockCloud., and . Concurr. Comput. Pract. Exp., 26 (4): 907-916 (2014)Accelerating virtual high-throughput ligand docking: current technology and case study on a petascale supercomputer., , , , and . Concurr. Comput. Pract. Exp., 26 (6): 1268-1277 (2014)Convex-hull voting method on a large data set., , and . BMC Bioinform., 16 (S15): P2 (December 2015)Open Data and transparency in artificial intelligence and machine learning: A new era of research., , and . F1000Research, (2023)