SEI: Virtual Screening Algorithms for Bioactive Compounds Based on Frequent Substructures

SEI：基于频繁子结构的生物活性化合物虚拟筛选算法

• 批准号: 0431135
• 负责人: George Karypis
• 金额: --
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0431135&HistoricalAwards=false
• 关键词: SEI; Virtual; Screening; Algorithms; Bioactive

AWARD ABSTRACT This award provides funding for the development of effective and efficient algorithms to analyze large chemical compound databases and identify the compounds that are the most probable for displaying the desired drug-like behavior. These virtual screening algorithms are based on a substructure-based classification framework that utilizes (i) highly efficient frequent subgraph discovery algorithms that mine the chemical compounds to discover all the substructures (topological or geometric) that are critical for the classification task, (ii) sophisticated feature selection and generation algorithms that combine multiple criteria to identify and synthesize a set of substructure-based features that simultaneously simplify the representation of the original compounds while retaining and exposing their key features, and (iii) kernel-based approaches that take into account the relationships between these substructures at different levels of granularity and complexity. The research is integrated with an educational plan that focuses on initiating undergraduate and graduate students to the various computational and data analysis aspects of virtual screening, machine learning, and data mining through courses, summer institutes, and research opportunities.The successful completion of this project will lead to advances in the drug development process by developing computationally efficient and accurate classification algorithms that can be used to replace or supplement biological-assay-based high-throughput screening (HTS) techniques and by producing a general purpose chemical compound classification software toolkit that will contain high-quality implementations of the various algorithms that will be developed and made available to the public. The combination of existing HTS-based approaches with these virtual screening methods will allow a move away from purely random-based testing, toward more meaningful and directed iterative rapid-feedback searches of subsets and focused libraries.

奖项摘要该奖项为开发有效和高效的算法提供资金，以分析大型化合物数据库并识别最有可能显示所需药物样行为的化合物。这些虚拟筛选算法基于基于子结构的分类框架，其利用（i）高效的频繁子图发现算法，其挖掘化合物以发现所有子结构（拓扑的或几何的）对于分类任务是关键的，（ii）复杂的特征选择和生成算法，其结合联合收割机多个标准以识别和合成一组子结构-的功能，同时简化表示的原始化合物，同时保留和暴露其关键功能，以及（iii）基于内核的方法，考虑到这些子结构之间的关系，在不同的粒度和复杂程度。该研究与一项教育计划相结合，该计划侧重于通过课程，暑期研究所，该项目的成功完成将通过开发计算效率高且准确的分类算法，用于替代或补充基于生物测定的高通量筛选（HTS）技术，并通过生产通用化合物分类软件工具包，其中包含将开发并向公众提供的各种算法的高质量实现。将现有的基于HTS的方法与这些虚拟筛选方法相结合，将允许从纯粹基于随机的测试转向更有意义和定向的子集和集中库的迭代快速反馈搜索。