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首页> 外文期刊>Journal of Computer-Aided Molecular Design >Representation of molecular structure using quantum topology with inductive logic programming in structure-activity relationships
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Representation of molecular structure using quantum topology with inductive logic programming in structure-activity relationships

机译：使用量子拓扑和归纳逻辑编程以分子-结构关系表示分子结构

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The requirement of aligning each individual molecule in a data set severely limits the type of molecules which can be analysed with traditional structure activity relationship (SAR) methods. A method which solves this problem by using relations between objects is inductive logic programming (ILP). Another advantage of this methodology is its ability to include background knowledge as 1st-order logic. However, previous molecular ILP representations have not been effective in describing the electronic structure of molecules. We present a more unified and comprehensive representation based on Richard Bader's quantum topological atoms in molecules (AIM) theory where critical points in the electron density are connected through a network. AIM theory provides a wealth of chemical information about individual atoms and their bond connections enabling a more flexible and chemically relevant representation. To obtain even more relevant rules with higher coverage, we apply manual postprocessing and interpretation of ILP rules. We have tested the usefulness of the new representation in SAR modelling on classifying compounds of low/high mutagenicity and on a set of factor Xa inhibitors of high and low affinity.
机译：对齐数据集中的每个分子的要求严重限制了可以使用传统结构活性关系（SAR）方法进行分析的分子类型。通过使用对象之间的关系解决此问题的方法是归纳逻辑编程（ILP）。这种方法的另一个优点是它能够将背景知识作为一阶逻辑包含在内。但是，先前的分子ILP表示在描述分子的电子结构方面并不有效。我们基于理查德·巴德（Richard Bader）的分子中的量子拓扑原子（AIM）理论提出一种更统一，更全面的表示形式，其中电子密度的临界点通过网络连接。 AIM理论提供了有关单个原子及其键连接的大量化学信息，从而可以实现更加灵活和化学相关的表示。为了获得更高覆盖率的更相关规则，我们应用了手动后处理和ILP规则的解释。我们已经测试了新模型在低/高致突变性化合物分类和一组高和低亲和力因子Xa抑制剂分类中的SAR建模的有用性。

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