Classification Algorithms for Chemical Compounds

化合物的分类算法

• 批准号: 7127208
• 负责人: GEORGE KARYPIS
• 金额: $ 27.64万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-30 至 2009-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7127208
• 关键词: artificial intelligence; bioengineering /biomedical engineering; bioinformatics; chemical structure; computational biology; computer program /software; computer system design /evaluation; conformation; drug classification; mathematical model; mathematics

Computational techniques that build models to correctly assign chemical compounds to various classes of interests have extensive applications in pharmaceutical research and are used extensively at various phases during the drug development process. These techniques are used to solve a number of classification problems such as predicting whether or not a chemical compound has the desired biological activity, is toxic or non-toxic, and filtering out drug-like compounds from large compound libraries. The overall goal of this proposal is to develop substructure-based classification algorithms for chemical compound datasets. The key elements of these algorithms are that they (i) utilize highly efficient substructure discovery algorithms to mine the chemical compounds and discover all substructures that can be critical for the classification task, (ii) use multiple criteria to generate a set of substructure-based features that simultaneously simplify the compounds' representation while retaining and exposing the features that are responsible for the specific classification problem, and (iii) build predictive models by employing kernel-based methods that take into account the relationships between these substructures at different levels of granularity and complexity, as well as information provided by traditional descriptors.

构建模型以正确分配化学化合物为各种兴趣的计算技术在药物研究中具有广泛的应用，并在药物开发过程中在各个阶段广泛使用。这些技术用于解决许多分类问题，例如预测化合物是否具有所需的生物学活性，有毒或无毒，并从大型化合物库中滤除类似药物的化合物。 该提案的总体目标是开发基于化学化合物数据集的基于子结构的分类算法。这些算法的关键要素是，它们（i）利用高效的子结构发现算法来挖掘化学化合物并发现所有可能对分类任务至关重要的子结构，（ii）使用多个标准来使用一组基于子结构来生成一组基于子结构的特征，以简化化合物的特定范围，同时又有特定的表现，同时又有ii的特定图表，即II，ii，同时进行了ii的特定型号，该功能（ii）具有ii的特定范围，该功能（ii）具有ii的特定范围，该功能（采用基于内核的方法来考虑这些子结构在不同级别的粒度和复杂性以及传统描述符提供的信息之间的关系。