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Schmidt B, Hildebrandt A. From GPUs to AI and quantum: three waves of acceleration in bioinformatics. Drug Discov Today 2024; 29:103990. [PMID: 38663581 DOI: 10.1016/j.drudis.2024.103990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 04/05/2024] [Accepted: 04/17/2024] [Indexed: 05/01/2024]
Abstract
The enormous growth in the amount of data generated by the life sciences is continuously shifting the field from model-driven science towards data-driven science. The need for efficient processing has led to the adoption of massively parallel accelerators such as graphics processing units (GPUs). Consequently, the development of bioinformatics methods nowadays often heavily depends on the effective use of these powerful technologies. Furthermore, progress in computational techniques and architectures continues to be highly dynamic, involving novel deep neural network models and artificial intelligence (AI) accelerators, and potentially quantum processing units in the future. These are expected to be disruptive for the life sciences as a whole and for drug discovery in particular. Here, we identify three waves of acceleration and their applications in a bioinformatics context: (i) GPU computing, (ii) AI and (iii) next-generation quantum computers.
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Affiliation(s)
- Bertil Schmidt
- Institut für Informatik, Johannes Gutenberg University, Mainz, Germany.
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Ruiz Echartea ME, Ritchie DW, Chauvot de Beauchêne I. Using restraints in
EROS‐DOCK
improves model quality in pairwise and multicomponent protein docking. Proteins 2020; 88:1121-1128. [DOI: 10.1002/prot.25959] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 03/04/2020] [Accepted: 05/27/2020] [Indexed: 12/26/2022]
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Wen C, Yan X, Gu Q, Du J, Wu D, Lu Y, Zhou H, Xu J. Systematic Studies on the Protocol and Criteria for Selecting a Covalent Docking Tool. Molecules 2019; 24:molecules24112183. [PMID: 31185706 PMCID: PMC6600387 DOI: 10.3390/molecules24112183] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Revised: 06/06/2019] [Accepted: 06/07/2019] [Indexed: 12/28/2022] Open
Abstract
With the resurgence of drugs with covalent binding mechanisms, much attention has been paid to docking methods for the discovery of targeted covalent inhibitors. The existence of many available covalent docking tools has inspired development of a systematic and objective procedure and criteria with which to evaluate these programs. In order to find a tool appropriate to studies of a covalently binding system, protocols and criteria are proposed for protein–ligand covalent docking studies. This paper consists of three sections: (1) curating a standard data set to evaluate covalent docking tools objectively; (2) establishing criteria to measure the performance of a tool applied for docking ligands into a complex system; and (3) creating a protocol to evaluate and select covalent binding tools. The protocols were applied to evaluate four covalent docking tools (MOE, GOLD, CovDock, and ICM-Pro) and parameters affecting covalent docking performance were investigated.
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Affiliation(s)
- Chang Wen
- Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou 510006, China.
| | - Xin Yan
- Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou 510006, China.
| | - Qiong Gu
- Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou 510006, China.
| | - Jiewen Du
- Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou 510006, China.
| | - Di Wu
- National Supercomputer Center in Guangzhou & School of Data and Computer Science, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou 510006, China.
| | - Yutong Lu
- National Supercomputer Center in Guangzhou & School of Data and Computer Science, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou 510006, China.
| | - Huihao Zhou
- Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou 510006, China.
| | - Jun Xu
- Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou 510006, China.
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