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

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类药物行为的化合物。这些虚拟筛选算法基于基于子结构的分类框架，该框架利用(I)高效的频繁子图发现算法来挖掘化合物以发现对分类任务至关重要的所有子结构(拓扑或几何)，(Ii)复杂的特征选择和生成算法，其结合多个准则来识别和合成一组基于子结构的特征，这些特征同时简化了原始化合物的表示，同时保留和揭示其关键特征，以及(Iii)基于核的方法，其考虑了这些子结构之间在不同粒度和复杂性水平上的关系。这项研究与一项教育计划相结合，该计划侧重于通过课程、暑期研习班和研究机会启动本科生和研究生对虚拟筛选、机器学习和数据挖掘的各种计算和数据分析方面的知识。该项目的成功完成将通过开发计算高效和准确的分类算法来取代或补充基于生物分析的高通量筛选(HTS)技术，并通过生产通用化合物分类软件工具包，该工具包将包含各种算法的高质量实现，并将被开发并向公众提供，从而推动药物开发过程的进步。现有的基于HTS的方法与这些虚拟筛选方法的结合将允许从纯粹基于随机的测试转向对子集和聚焦文库的更有意义和定向的迭代快速反馈搜索。

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.