Chemical Pattern Detection and Visualization in Biological Networks

生物网络中的化学模式检测和可视化

• 批准号: 8209015
• 负责人: TUDOR I OPREA
• 金额: $ 18.88万
• 依托单位: UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-01-01 至 2013-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8209015
• 关键词: Abbreviations; Advanced Development; Algorithms; Area; Binding; Biological; Biology; Biomedical Research; Chemical Structure; Chemicals; Chemistry; Communication; Communities; Computational Science; Data; Data Sources; Databases; Detection; Development; Extensible Markup Language; Goals; Graph; Grouping; Health; Housing; Human; Imagery; Internet; Ligands; Link; Maps; Methods; Mining; Molecular; Mus; Names; Network-based; Outcome; Outcomes Research; Output; Pattern; Pattern Recognition; Procedures; Process; Protocols documentation; PubChem; Rattus; Research; Research Personnel; Scientist; Signal Transduction; Solutions; Source; Speed; Structure-Activity Relationship; System; Technology; Testing; United States National Institutes of Health; Update; Variant; base; cheminformatics; computerized data processing; design; empowered; experience; heuristics; improved; innovation; insight; novel; open source; programs; public health relevance; response; scaffold; small molecule; small molecule libraries; tool

DESCRIPTION (provided by applicant): While the massive amount of molecular bioactivity data creates new opportunities, it also hinders the way scientists conduct biomedical research due to the inherent difficulty of processing many separate and heterogeneous data sources. The quality and type of data input often limits the project outcome. To improve research outcome, access to all available data and multiple alternative hypothesis testing are essential. Targeting less experienced end-users, we will develop tools that facilitate "jumps" in the small molecule / bioactivity / biomedical data area, leading from one potential solution to another, encouraging users to explore multiple, alternate hypotheses. We will integrate data from multiple bioactivity databases, including PubChem, ChemBank, ChEMBL, PDSP and WOMBAT, into one centralized system. We will develop advanced chemical pattern recognition algorithms and deliver a Cytoscape-based visualization tool for the global exploration of relationships between chemical patterns and biological activities/targets. We will achieve this via three Specific Aims: 1. Create one simple unified interface for many heterogeneous databases, CARLSBAD (Confederated Annotated Research Libraries of Small molecule Biological Activity Data); the data will reconcile small molecule bioactivity data across multiple sources for human, rat and mouse targets. 2. Develop advanced algorithms for chemical pattern detection and annotation; we will detect the Maximum Overlapping Set (MOS) and HierS (hierarchical scaffolds) and annotate chemicals in CARSLBAD accordingly. 3. Develop a Cytoscape plugin for the visualization and exploration of chemical pattern bioactivity networks. Via MOS/HierS patterns, users will be able to identify target specific chemical signatures (determinants for activity and selectivity); in the absence of specific signals, these patterns will serve as rationale for off- target and promiscuous bioactivity prediction. Storing unique target-ligand bioactivity data as well as chemical patterns, CARLSBAD will be designed, implemented and maintained on an enterprise platform for use by the scientific community. The new Cytoscape plugin will integrate with existing core components and plugins to bridge across chemistry and biology in a multi-disciplinary manner. PUBLIC HEALTH RELEVANCE: The proposed research aims to empower the chemistry and biology research community with an innovative, network-based tool for mining vast amounts of chemical and biological data. It will provide an effective and improved way for researchers to evaluate, visualize and explore small molecule bioactivity data in a multi-disciplinary manner, thus leading to improved output in human health research.

描述（由申请人提供）：虽然大量分子生物活性数据创造了新的机会，但由于处理许多独立且异构的数据源的固有困难，它也阻碍了科学家进行生物医学研究的方式。数据输入的质量和类型通常会限制项目结果。为了改善研究结果，必须访问所有可用数据和多个替代假设检验。针对经验较低的最终用户，我们将开发在小分子 /生物活性 /生物医学数据领域中促进“跳跃”的工具，从一个潜在的解决方案通往另一种解决方案，鼓励用户探索多个替代假设。我们将将来自多个生物活性数据库的数据（包括Pubchem，Chembank，Chembl，PDSP和Wombat）整合到一个集中式系统中。我们将开发先进的化学模式识别算法，并提供基于细胞尺度的可视化工具，以全球探索化学模式与生物活动/目标之间的关系。我们将通过三个特定目标来实现这一目标：1。为许多异质数据库创建一个简单的统一界面，即Carlsbad（小分子生物学活动数据的同盟带注释的研究库）；这些数据将调和人，大鼠和小鼠靶标的多个来源的小分子生物活性数据。 2。开发用于化学模式检测和注释的晚期算法；我们将相应地检测到Carslbad中的最大重叠集（MOS）和HIER（分层支架）和注释化学物质。 3。开发一个可视化和探索化学模式生物活性网络的胞质插件。通过MOS/HIERS模式，用户将能够识别目标特定的化学特征（活动和选择性的决定因素）；在没有特定信号的情况下，这些模式将作为非目标和滥交生物活性预测的理由。 Carlsbad将在企业平台上设计，实施和维护，以存储独特的目标配体生物活性数据以及化学模式，科学界将设计，实施和维护。新的Cytoscape插件将与现有的核心组件和插件集成，以多学科的方式桥接化学和生物学。 公共卫生相关性：拟议的研究旨在使用一种创新的，基于网络的工具来挖掘大量化学和生物学数据。它将为研究人员提供一种有效而改进的方式，以多学科的方式评估，可视化和探索小分子生物活性数据，从而改善人类健康研究的产量。

项目成果

SmartGraph: a network pharmacology investigation platform.

SmartGraph：网络药理学研究平台。

• DOI: 10.1186/s13321-020-0409-9
• 发表时间: 2020
• 期刊: Journal of cheminformatics
• 影响因子: 8.6
• 作者: Zahoránszky-Kőhalmi,Gergely;Sheils,Timothy;Oprea,TudorI
• 通讯作者: Oprea,TudorI

ChemProt-2.0: visual navigation in a disease chemical biology database.

• DOI: 10.1093/nar/gks1166
• 发表时间: 2013-01
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Kim Kjærulff S;Wich L;Kringelum J;Jacobsen UP;Kouskoumvekaki I;Audouze K;Lund O;Brunak S;Oprea TI;Taboureau O
• 通讯作者: Taboureau O

The CARLSBAD database: a confederated database of chemical bioactivities.

• DOI: 10.1093/database/bat044
• 发表时间: 2013
• 期刊: Database : the journal of biological databases and curation
• 作者: Mathias SL;Hines-Kay J;Yang JJ;Zahoransky-Kohalmi G;Bologa CG;Ursu O;Oprea TI
• 通讯作者: Oprea TI