Knowledge discovery on chemical reactivity from experimental reaction information

被引：0

作者：

Satoh, Hiroko ^{[1
,2
]}

Nakata, Tadashi ^{[2
]}

机构：

[1] Artificial Intelligence Systems Division, National Institute of Informatics, Chiyoda, Tokyo 101-8430

[2] Synthetic Organic Chemistry Laboratory, RIKEN, Wako, Saitama 351-0198

来源：

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 2003年 / 2843卷

关键词：

Computers;

D O I：

10.1007/978-3-540-39644-4_48

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A knowledge discovery approach from chemical information with focusing on negative information in positive data is described. Reported experimental chemical reactions are classified into some reaction groups according to similarities in physicochemical features with a self-organizing mapping(SOM) method. In one of the reaction groups, functional groups of reactants are divided into two categories according to the experimental results whether they reacted or not. The classes of the functional groups are used for derivation of knowledge on chemical reactivity and condition intensity. The approach is demonstrated with a model dataset. © Springer-Verlag Berlin Heidelberg 2003.

引用

页码：470 / 477

页数：7

共 14 条

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