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中药化学结构分类在化学类数据库检索、药物设计及数据挖掘分析过程中都发挥重要作用。目的:对数据库中收集的化学结构信息进行分类标引,以提高检索效率便于数据管理与知识挖掘。方法:采用可用于互联网和在线服务的SMARTS、SMILES文本编码技术的中药化学结构分类方法。结果:实现了对《中华本草》的1万多的化学结构进行自动分类。结论:基于SMARTS、SMILES文本编码技术的化学结构分类方法可行且简便易学。
The chemical structure classification of traditional Chinese medicine plays an important role in chemical database searching, drug design and data mining analysis. Objective: To classify and index the chemical structure information collected in the database to improve the retrieval efficiency and to facilitate data management and knowledge mining. Methods: The Chinese chemical structure classification method using SMARTS, SMILES text encoding technology that can be used for Internet and online services. Results: The automatic classification of more than 10,000 chemical structures of “Chinese Materia Medica” has been achieved. Conclusion: Based on SMARTS, SMILES text encoding technology is feasible and easy to learn.