GPU-Accelerated Parallel Computer for Drug Discovery Applications

用于药物发现应用的 GPU 加速并行计算机

基本信息

批准号：
8826249
负责人：
Jarrod Anson Smith
金额：
$ 22.49万
依托单位：
VANDERBILT UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-03-01 至 2017-02-28
项目状态：
已结题

项目摘要

DESCRIPTION: We request funds to purchase a GPU-accelerated parallel computer to provide financially sustainable support for ongoing drug discovery efforts at Vanderbilt University. The proposed system consists of 20 Dell R720 compute nodes each equipped with dual nVidia K20 Tesla GPUs, 64GB of RAM, and dual 6-core Intel Xeon 2620 CPUs. The system will be made available to the Vanderbilt drug discovery research community through our Computational Structural and Chemical Biology Core (CSCBC), which offers a comprehensive menu of state-of-the-art molecular modeling techniques using either a self-service or fee-for-service consulting model. Expertise in these areas is provided by highly qualified staff scientists and PIs and in the Center for Structural Biology (CSB) and the Vanderbilt Institute for Chemical Biology (VICB). Fundamental to our approach is the recognition that successful application of computer modeling is dependent upon obtaining a sufficient quality and quantity of experimental data as inputs. The CSCBC therefore works closely, in a supporting role, with complimentary experimental cores such as the Crystallography, NMR, EPR, and Cryo-EM facilities sponsored by the CSB, and the High Throughput Screening and Chemical Synthesis cores sponsored by the VICB. For several years, GPUs have promised to dramatically accelerate software applications that can be organized to run on a simplified but highly parallel architecture, and do so at a relatively low cost when compared with traditional, more complex CPUs which are easier to program but also relatively more expensive per unit of raw computing power. Redesigning and reprogramming compute codes so that they can take advantage of GPU architectures remains an active area in computer science. However, many codes have begun to move out of a research and development phase into a "production" phase with broad adoption by end-users who are eager to leverage the price/performance advantage offered by GPUs. Virtual screening with quantitative structure activity relationships (QSAR) is one such mature application that has the potential to dramatically increase the number of active compounds that are identified in NIH-funded drug discovery projects at Vanderbilt. BCL:CHEMINFO is a GPU-accelerated ligand-based virtual screening application that can enrich the number of active hits in high-throughput screening assays by 30x-60x. BCL:CHEMINFO runs between 150x-300x faster on GPUs vs. CPUs, but it only costs about 2x to purchase GPU-equipped computers. Building the proposed GPU-accelerated computer to support drug discovery projects will significantly decrease the costs and time associated with discovering new molecules of therapeutic interest. The proposed system will be maintained at the Advanced Computing Center for Research and Education (ACCRE), which has a large 24/7 on-call staff with extensive experience managing clusters of CPU and GPU- based computers in support of biomedical research. The technical support at ACCRE will be largely covered by institutional matching funds, further reducing costs to the major and minor users on this proposal.

描述：我们要求资金购买一台GPU加速平行计算机，以为范德比尔特大学正在进行的药物发现工作提供财务可持续的支持。所提出的系统由20个Dell R720计算节点组成，每个节点配备了双Nvidia K20 Tesla GPU，64GB RAM和双6核Intel Xeon 2620 CPU。该系统将通过我们的计算结构和化学生物学核心（CSCBC）提供给范德比尔特药物发现研究界，该核心（CSCBC）使用自助服务或费用服务咨询模型提供了最先进的分子建模技术菜单。这些领域的专业知识由高素质的员工科学家和PI提供结构生物学中心（CSB）和范德比尔特化学生物学研究所（VICB）。我们方法的基础是认识到，成功应用计算机建模取决于获得足够的实验数据作为输入的足够质量和数量。因此，CSCBC在辅助作用中紧密地工作，与CSB发起的晶体学，NMR，EPR和冷冻EM设施等免费实验核，以及由VICB赞助的高吞吐量筛选和化学合成核心。几年来，GPU已承诺将大大加速可以组织起来以更简单但高度平行的体系结构运行的软件应用程序，并且与传统，更复杂的CPU相比，该应用程序的成本相对较低，这些CPU易于编程，但易于编程，但相对较昂贵的每单位原始计算能力。重新设计和重新编程的计算代码，以便它们可以利用GPU架构仍然是计算机科学领域的活跃领域。但是，许多代码已开始从研发阶段逐步进入一个“生产”阶段，最终用户渴望利用GPU提供的价格/绩效优势的最终用户采用。具有定量结构活动关系（QSAR）的虚拟筛查是一种成熟的应用，具有显着增加范德比尔特NIH资助的药物发现项目中确定的活性化合物的数量。 BCL：ChemInfo是一种基于GPU的基于配体的虚拟筛选应用程序，可以将高通量筛选测定中的主动击球数量丰富30 x-60x。 BCL：ChemInfo在GPU与CPU上的运行速度更快，但购买配备GPU的计算机的价格仅为2倍。构建建议的GPU加速计算机来支持药物发现项目将大大降低与发现新理论分子相关的成本和时间。拟议的系统将维护在高级研究与教育中心（ACPRE），该中心拥有大量的24/7全天候员工，并具有丰富的经验，可以管理CPU和基于GPU的计算机组，以支持生物医学研究。机构匹配资金将在很大程度上涵盖ACCRE的技术支持，从而进一步降低了该提案的主要用户和次要用户的成本。

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Molecular dissection of effector mechanisms of RAS-mediated resistance to anti-EGFR antibody therapy

DOI：
10.18632/oncotarget.17438
发表时间：
2017-07-11
期刊：
ONCOTARGET
影响因子：
0
作者：
Kasper, Stefan;Reis, Henning;Schuler, Martin
通讯作者：
Schuler, Martin

BCL::Conf: small molecule conformational sampling using a knowledge based rotamer library.

DOI：
10.1186/s13321-015-0095-1
发表时间：
2015
期刊：
Journal of cheminformatics
影响因子：
8.6
作者：
Kothiwale S;Mendenhall JL;Meiler J
通讯作者：
Meiler J

Rearrangement of the Extracellular Domain/Extracellular Loop 1 Interface Is Critical for Thyrotropin Receptor Activation.

细胞外结构域/细胞外环 1 界面的重排对于促甲状腺素受体激活至关重要。

DOI：
10.1074/jbc.m115.709659
发表时间：
2016
期刊：
The Journal of biological chemistry
影响因子：
0
作者：
Schaarschmidt,Joerg;Nagel,MarcusBM;Huth,Sandra;Jaeschke,Holger;Moretti,Rocco;Hintze,Vera;vonBergen,Martin;Kalkhof,Stefan;Meiler,Jens;Paschke,Ralf
通讯作者：
Paschke,Ralf

Incorporating a Thiophosphate Modification into a Common RNA Tetraloop Motif Causes an Unanticipated Stability Boost.

DOI：
10.1021/acs.biochem.0c00685
发表时间：
2020-12
期刊：
Biochemistry
影响因子：
2.9
作者：
P. Pallan;T. Lybrand;M. Schlegel;J. Harp;Hartmut Jahns;M. Manoharan;M. Egli
通讯作者：
P. Pallan;T. Lybrand;M. Schlegel;J. Harp;Hartmut Jahns;M. Manoharan;M. Egli

Ancient human miRNAs are more likely to have broad functions and disease associations than young miRNAs.