Structure-activity relationships derived by machine learning: The use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming
Structure-activity relationships derived by machine learning: The use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming
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DOI：
10.1073/pnas.93.1.438
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发表时间：
1996-01-09
影响因子：
11.1
通讯作者：
Sternberg, MJE
中科院分区：
文献类型：
--
作者：
King, RD;Muggleton, SH;Sternberg, MJE
我们提出了形成结构-活性关系(SARS)的一般方法。这种方法是基于用原子及其键的连接性来表示化学结构，并结合归纳逻辑编程(ILP)算法PROGOL。现有的合成孔径雷达方法通过使用对象的一般属性来描述化学结构，不可能直接将化学结构映射到基于属性的描述，因为这种描述没有内部组织，更自然和一般的方法是使用关系描述，其中描述的内部结构映射被描述对象的内部结构。我们的原子和键连接性表示是一个关系描述，ILP算法可以形成具有关系描述的SARS，我们通过调查230个芳香族和杂芳族硝基化合物的SARS来测试关系方法，这些化合物以前被分成两个子集，188个化合物可以回归，对于188个化合物，发现SBR与最好的统计或神经网络生成的SARS一样准确，PROGOL SAR的优点是它不需要使用专家手工制作的任何指示变量，生成的规则很容易理解。对于42种化合物，PROGOL形成了比线性回归、二次回归和反向传播显著(P<；0.025)更准确的合成孔径雷达。这种合成孔径雷达是基于自动生成的致突变性结构性警报。
We present a general approach to forming structure-activity relationships (SARs). This approach is based on representing chemical structure by atoms and their bond connectivities in combination with the inductive logic programming (ILP) algorithm PROGOL. Existing SAR methods describe chemical structure by using attributes which are general properties of an object, It is not possible to map chemical structure directly to attribute-based descriptions, as such descriptions have no internal organization, A more natural and general way to describe chemical structure is to use a relational description, where the internal construction of the description maps that of the object described. Our atom and bond connectivities representation is a relational description, ILP algorithms can form SARs with relational descriptions, We have tested the relational approach by investigating the SARs of 230 aromatic and heteroaromatic nitro compounds, These compounds had been split previously into two subsets, 188 compounds that were amenable to regression and 42 that were not, For the 188 compounds, a SBR was found that was as accurate as the best statistical or neural network-generated SARs, The PROGOL SAR has the advantages that it did not need the use of any indicator variables handcrafted by an expert, and the generated rules were easily comprehensible. For the 42 compounds, PROGOL formed a SAR that was significantly (P < 0.025) more accurate than linear regression, quadratic regression, and back-propagation. This SAR is based on an automatically generated structural alert for mutagenicity.