NIDA Center of Excellence OF Computational Drug Abuse Research (CDAR)

NIDA 计算药物滥用研究卓越中心 (CDAR)

• 批准号: 8743368
• 负责人: Ivet Bahar
• 金额: $ 109.43万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8743368
• 关键词: Address; Algorithms; Area; Biological; Biology; Cannabinoids; Categories; Cells; Chemicals; Clinical Trials Network; Cloud Computing; Cocaine; Collaborations; Communities; Complement; Computational Biology; Computer software; Consultations; Data; Databases; Development; Doctor of Philosophy; Drug abuse; Effectiveness; Endocytosis; Ensure; Environmental Risk Factor; Feedback; Fostering; Funding; Genetic; Genome; Genomics; Genotype; Goals; Human Genome; Intervention; Investigation; Joints; Leadership; Link; Machine Learning; Maps; Mentors; Methods; Modeling; Molecular; N-Methyl-D-Aspartate Receptors; National Institute of Drug Abuse; Neuropharmacology; Pharmaceutical Preparations; Pharmacologic Substance; Pharmacology; Phenotype; Proteins; Regulation; Research; Research Activity; Research Personnel; Research Project Grants; Research Support; Resources; Science; Signal Transduction; Software Tools; Structure; Substance Use Disorder; System; Systems Biology; Technology; Therapeutic; Training; Translating; Translational Research; Universities; addiction; base; biobehavior; cheminformatics; cloud based; computational chemistry; computer science; data mining; design; dopamine transporter; drug abuse prevention; epigenetic marker; falls; genome-wide; improved; insight; knowledge base; member; novel; operation; predictive modeling; prevent; professor; receptor; repository; tool

DESCRIPTION (provided by applicant): We propose to establish a NIDA Center of Excellence for Computational Drug Abuse Research (CDAR) between the University of Pittsburgh (Pitt) and (CMU), with the goal of advancing and ensuring the productive and broad usage of state-of-the-art computational technologies that will facilitate and enhance drug abuse (DA) research, both in the local (Pittsburgh) area and nationwide. To this end, we will develop/integrate tools for DA-domain-specific chemical-to-protein-to-genomics mapping using cheminformatics, computational biology and computational genomics methods by centralizing computational chemical genomics (or chemogenomics) resources while also making them available on a cloud server. The Center will foster collaboration and advance knowledge-based translational research and increase the effectiveness of ongoing funded research project (FRPs) via the following Research Support Cores: (1) The Computational Chemogenomics Core for DA (CC4DA) will help address polydrug addiction/polypharmacology by developing new chemogenomics tools and by compiling the data collected/generated, along with those from other Cores, into a DA knowledge-based chemogenomics (DA-KB) repository that will be made accessible to the DA community. (2) The Computational Biology Core (CB4DA) will focus on developing a resource for structure-based investigation of the interactions among substances of DA and their target proteins, in addition to assessing the drugability of receptors and transporters involved in DA and addiction. These activities will be complemented by quantitative systems pharmacology methods to enable a systems-level approach to DA research. (3) The Computational Genomics Core (CG4DA) will carry out genome-wide discovery of new DA targets, markers, and epigenetic influences using developed machine learning models and algorithms. (4) The Administrative Core will coordinate Center activities, provide management to oversee the CDAR activities in consultation with the Scientific Steering Committee (SSC) and an External Advisory Board (EAB), ensure the effective dissemination of software/data among the Cores and the FRPs, and establish mentoring mechanisms to train junior researchers. Overall, the Center will strive to achieve the long-term goal of translating advances in computational chemistry, biology and genomics toward the development of novel personalized DA therapeutics.

描述（由申请人提供）： 我们提议在匹兹堡大学 (Pitt) 和 (CMU) 之间建立 NIDA 计算药物滥用研究卓越中心 (CDAR)，其目标是推进和确保最先进技术的高效和广泛使用计算技术将促进和加强当地（匹兹堡）地区和全国的药物滥用（DA）研究。为此，我们将通过集中计算化学基因组学（或化学基因组学）资源并使其可用，使用化学信息学、计算生物学和计算基因组学方法开发/集成用于 DA 域特定化学到蛋白质到基因组学映射的工具在云服务器上。该中心将通过以下研究支持核心促进合作并推进基于知识的转化研究，并提高正在进行的资助研究项目 (FRP) 的有效性：(1) DA 计算化学基因组学核心 (CC4DA) 将帮助解决多种药物成瘾/多种药理学问题通过开发新的化学基因组学工具，并将收集/生成的数据与其他核心的数据一起编译到可访问的基于 DA 知识的化学基因组学 (DA-KB) 存储库中到 DA 社区。 (2) 计算生物学核心（CB4DA）将重点开发一种资源，用于基于结构的研究 DA 物质与其靶蛋白之间的相互作用，此外还评估与 DA 和成瘾相关的受体和转运蛋白的成药性。这些活动将得到定量系统药理学方法的补充，以实现 DA 研究的系统级方法。 (3) 计算基因组学核心 (CG4DA) 将使用已开发的机器学习模型和算法，在全基因组范围内发现新的 DA 靶标、标记和表观遗传影响。 (4) 行政核心将协调中心的活动，与科学指导委员会 (SSC) 和外部顾问委员会 (EAB) 协商，提供管理监督 CDAR 的活动，确保软件/数据在核心和FRP，并建立指导机制来培训初级研究人员。总体而言，该中心将努力实现将计算化学、生物学和基因组学的进步转化为新型个性化 DA 疗法的开发的长期目标。