Ros F, Audouze K, Pintore M, Chrétien J R
Laboratory of Chemometrics and BioInformatics, University of Orléans, France.
SAR QSAR Environ Res. 2000;11(3-4):281-300. doi: 10.1080/10629360008033236.
Kohonen neural networks, also known as Self Organizing Map (SOM), offer a useful 2D representation of the compound distribution inside a large chemical database. This distribution results from the compound organization in a molecular diversity hyperspace derived from a large set of molecular descriptors. Fuzzy techniques based on the "concept of partial truth" reveal to be also a valuable tool for the direct exploitation of chemical databases or SOM. In such cases a fuzzy clustering algorithm is used. In this paper, a complete hybrid system, combining SOM and fuzzy clustering, is applied. As example, a series of olfactory compounds was selected. The complexity of such information is that a same compound may exhibit different odors. It is shown how fuzzy logic helps to have a better understanding of the organization of the compounds. These hybrid systems, using simultaneously SOM and fuzzy clustering, are foreseen as powerful tools for "virtual pre-screening".
科霍宁神经网络，也称为自组织映射（SOM），可对大型化学数据库中的化合物分布提供有用的二维表示。这种分布源于化合物在由大量分子描述符导出的分子多样性超空间中的组织方式。基于“部分真值概念”的模糊技术也被证明是直接利用化学数据库或SOM的宝贵工具。在这种情况下，会使用模糊聚类算法。本文应用了一个结合SOM和模糊聚类的完整混合系统。例如，选择了一系列嗅觉化合物。此类信息的复杂性在于同一化合物可能呈现不同气味。展示了模糊逻辑如何有助于更好地理解化合物的组织方式。这些同时使用SOM和模糊聚类的混合系统被视为“虚拟预筛选”的强大工具。
