DEVELOPMENT OF A GRAPH STRUCTURE DATAMINING METHOD AND IDENTIFICATION SYSTEM OF ACTIVE MOLECULE SUBSTRU CTURES

活性分子亚结构图结构数据挖掘方法及识别系统的开发

基本信息

批准号：
12480088
负责人：
WASHIO Takashi
金额：
$ 9.86万
依托单位：
OSAKA UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2000
资助国家：
日本
起止时间：
2000 至 2002
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-12480088/
关键词：
GRAPH STRUCTURE DATA MINING ADJACENCY MATRIX CANONICAL FORM ISOMOPHISM MOLECULAR STRUCTURE CARCINOGENESITY MUNAGENESITY

项目摘要

In the first fiscal year, the theoretical framework of graph structure data mining was investigated, and a prototype system for active molecule substructure identification was developed. In this work, the representation of graph structure data in computers and search principle of characteristic graph patterns are studied. Them, the survey of techniques in chemistry which can be introduced to our work has been conducted, and these techniques were reflected in the prototype system. Finally, the basic performance of the prototype system has been evaluated through the substructure extraction in carcinogenetic and mutagenetic chemical component data.In the nest fiscal year, the framework of the graph structure data mining was extended to be more efficient in terms of computation time and memory consumption, and the real scale system for active molecule substructure identification has been developed. The algorithm for the efficient computation time and memory consumption was developed, and under the comparison with the conventional techniques in chemistry, the function of the real system was designed. Then, the principle and the algorithm of the real system was modified and extended to enable the graph structure data mining on the massive graph structure data.In the final fiscal year, further functions desired to be implemented in the view of chemical analysis were investigated based on the real system developed in the former year, and some functions which can be implemented feasibly were added to the real system. Then, from the view points of the chemical engineering and the computational theory, the practicality and the wide applicability of the real system have been evaluated. Through these evaluations, the practical and high performance of the developed real sysem has been confirmed. The effort to develop commercial system under collaboration with industries is currently underway.

第一财年研究了图结构数据挖掘的理论框架，开发了活性分子子结构识别的原型系统。本工作研究了图结构数据在计算机中的表示以及特征图模式的搜索原理。他们对可以引入到我们工作中的化学技术进行了调查，这些技术在原型系统中得到了体现。最后，通过致癌和致突变化学成分数据的子结构提取，对原型系统的基本性能进行了评估。在上个财年，对图结构数据挖掘的框架进行了扩展，使其在计算时间和效率方面更加高效。内存消耗，以及用于活性分子子结构识别的真实规模系统已经开发出来。开发了有效计算时间和内存消耗的算法，并在与化学中的常规技术进行比较的情况下，设计了实际系统的功能。然后，对实际系统的原理和算法进行修改和扩展，以实现对海量图结构数据的图结构数据挖掘。在最后一个财年，从化学分析的角度进一步研究了希望实现的功能。在前一年开发的真实系统的基础上，在真实系统中增加了一些可以切实实现的功能。然后，从化学工程和计算理论的角度，评估了实际系统的实用性和广泛适用性。通过这些评估，所开发的真实系统的实用性和高性能得到了证实。目前正在与行业合作开发商业系统。