The RPI Exploratory Center for Cheminformatics (RMI)

RPI 化学信息学探索中心 (RMI)

• 批准号: 7125575
• 负责人: Curt Mark Breneman
• 金额: $ 37.72万
• 依托单位: RENSSELAER POLYTECHNIC INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-23 至 2009-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7125575
• 关键词: Internet; NIH Roadmap Initiative tag; bioinformatics; chemical models; cheminformatics; computer program /software; computers; data collection methodology /evaluation; data management; information retrieval; interdisciplinary collaboration; model design /development; molecular biology

The purpose of this Exploratory Center for Cheminformatics Research (ECCR) P20 planning grant is to develop a mechanism for bringing together and stimulating collaborative pilot projects among a constantly-evolving nucleus of experts in Cheminformatics-related fields ranging from methods of encoding and capturing molecular information, to machine learning and data mining techniques, to predictive model development, validation, interpretation and utilization. In addition to these research efforts, the Center will bring together a set of domain specialists and application scientists who will serve as both data generators and end users of the knowledge provided by the molecular property models and modeling methods developed during the course of the grant. This group will also test the new Cheminformatics software that will constitute a tangible, deliverable product from this work. Ten application project modules that exemplify possible interactions between various groups and areas of expertise within the Center are presented as part of this proposal. The unifying vision behind the proposed Center is that much of what is done in each of the subdisciplines represented here can be expressed in a Cheminformatics context: The many diverse project areas can be grouped into one or more overlapping categories: "Data Generators" (those who use either theoretical or experimental methods for creating or extracting knowledge), "Machine Learning and Datamining" groups (who perform model validation, feature selection, pattern recognition, generation of potentials of mean force and knowledge-based potential work), as well as "Property-Prediction" groups (who perform chemically-aware model building, molecular property descriptor generation, Quantitative Structure-Property Relationship modeling, validation, and interpretation), and "Application" groups who utilize the information made available using the new tools and methods that are developed as part of the Center. It is our strong belief that these areas of expertise can be brought together within this Planning Grant proposal to generate something larger than the sum of the parts. The Exploratory Center will seed new interdisciplinary projects and train graduate students in these areas. Relevance: Advances in the generation, mining and analysis of chemical information is crucial to the development of new drug therapies, and to modern methods of bioinformatics and molecular medicine.

化学信息学研究探索中心 (ECCR) P20 的目的 规划拨款旨在开发一种机制，汇集和激励化学信息学相关领域不断发展的专家核心之间的协作试点项目，范围从编码和捕获分子信息的方法到机器学习和数据挖掘技术，再到预测模型开发、验证、解释和利用。除了这些研究工作之外，该中心还将汇集一组领域专家和应用科学家，他们将充当数据生成者和在资助过程中开发的分子特性模型和建模方法所提供的知识的最终用户。该小组还将测试新的化学信息学软件，该软件将构成这项工作的有形的、可交付的产品。十大应用项目模块 作为本提案的一部分，举例说明了中心内不同群体和专业领域之间可能进行的互动。拟议中心背后的统一愿景是，这里代表的每个子学科所做的大部分工作都可以在化学信息学背景下表达：许多不同的项目领域可以分为一个或多个重叠的类别：“数据生成器”（那些使用理论或实验方法来创建或提取知识）、“机器学习和数据挖掘”小组（执行模型验证、特征选择、模式识别、平均力势的生成和基于知识的势功），以及“性质预测”小组（执行化学感知模型构建、分子性质描述符生成、 定量结构-性质关系建模、验证和解释）和“应用”小组，他们利用作为中心一部分开发的新工具和方法提供的信息。我们坚信，这些领域的专业知识可以在这个规划拨款提案中汇集在一起​​，产生比各部分相加更大的成果。探索中心将培育新的跨学科项目并培训这些领域的研究生。 相关性：化学信息的生成、挖掘和分析方面的进步对于新药疗法的开发以及生物信息学和分子医学的现代方法至关重要。