Application of a Self-Organizing Map to Quantitative Structure-Activity Relationship Analysis of Carboquinone and Benzodiazepine

Abstract

Self-organizing map (SOM) of Kohonen seems to be a promising approach beyond the standard one to regression for some classification problems encountered in the field of pharmacy. We apply them, therefore, to the quantitative structure-activity relationship (QSAR) in carboquinones and benzodiazepines, and show their usefulness. Most QSAR analysis using neural networks has been made by adopting neural networks with supervised learning. On the contrary, SOM obeys unsupervised learning and originally does not involve the use of desired target data. If we note that an appreciable fraction of data may be missing without making the similarity comparison impossible in SOM if the number of attributes considered is appreciable, QSAR analysis using SOM is found to be possible as if supervised learning. Similar to target data in supervised learning, we can take into account target data (=observed activity) as one of attributes in addition to other attributes (=structural descriptors). Choice of optimal descriptors as input parameters was found to be essential to generate valuable SOM.