Structure activity relationships (SAR) and pharmacophore discovery using Inductive Logic Programming (ILP)
Structure activity relationships (SAR) and pharmacophore discovery using Inductive Logic Programming (ILP)
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DOI：
10.1002/qsar.200310005
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发表时间：
2003-07-01
期刊：
影响因子：
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通讯作者：
Muggleton, SH
中科院分区：
文献类型：
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作者：
Sternberg, MJE;Muggleton, SH
归纳逻辑编程（ILP），一种机器学习的形式，推导结构活性关系（SAR）和发现药效团的应用报告。ILP方法最初应用于模型ID SAR的分子属性。随后开发了描述化学连接性的2D ILP SAR。最后，ILP已被用来模拟三维SAR中的药效团的构象可以描述。ILP比许多其他广泛使用的方法具有优势，因为它可以推理关系，从而发现化学子结构和3D特征，而无需在学习之前明确编码这些方面。特别地，不需要结构叠加。此外，ILP的结果提供了药物化学家容易理解的化学描述。在一些试验中，基于ILP的SAR已被证明比广泛使用的方法显著更准确。
The application of Inductive Logic Programming (ILP), a form of machine learning, to derive structure activity relationships (SAR) and to discover pharmacophores is reported. The ILP approach was initially applied to model ID SARs in terms of the attributes of the molecules. Subsequently 2D ILP SARs were developed describing chemical connectivity. Finally ILP has been used to model 3D SARs in which the conformation of the pharmacophore can be described. ILP has advantages over many other widely used methods as it can reason with relations and hence discover chemical substructures and 3D features without these aspects having been explicitly encoded prior to learning. In particular, there is no requirement for a structural superposition. Additionally, the results of ILP provide chemical descriptions that can readily be understood by a medicinal chemist. In several trials, lLP-based SARs have been shown to be significantly more accurate than widely-used methods.