Application of Genetic Function Approximation to Qualitative Structure-Activity Relationships

遗传函数逼近在定性构效关系中的应用

• 批准号: 9362149
• 负责人: David Rogers
• 金额: $ 6.47万
• 依托单位: Molecular Simulations
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-01-01 至 1994-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9362149&HistoricalAwards=false
• 关键词: Application; Genetic; Function; Approximation; Qualitative

9362149 Rogers The goal of this project is to develop an improved model which will relate the biological activity of a compound to its physicochemical properties. This area of research is known as Quantitative Structure-Activity Relationship (QSAR) analysis. The QSAR is a tool which can be used by chemists in drug design to help reduce the current high level of testing in drug development. The approach to model development in this proposed research is based on the Genetic Function Approximation (GFA) algorithm. The GFA would replace the standard regression analysis normally used in the QSAR task. The objective is to construct higher- quality predictive models, even with data sets that may have thousands of parameters. ***

9362149罗杰斯这个项目的目标是开发一种改进的模型，将化合物的生物活性与其物理化学性质联系起来。这一研究领域被称为定量结构-活性关系(QSAR)分析。定量构效关系是化学家在药物设计中可以使用的一种工具，以帮助减少目前药物开发中的高测试水平。这项研究中的模型开发方法是基于遗传函数逼近(GFA)算法。GFA将取代QSAR任务中通常使用的标准回归分析。其目标是构建更高质量的预测模型，即使使用可能具有数千个参数的数据集。***