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Attribute Selection and Visualization for Classification for Small Molecules

The process of drug discovery using in silico methods often produces datasets with a very large number of attributes (fields) per instance (record). Automated classification of such data on properties such as toxicity provides significant benefits for drug design but must cope effectively with the large number of attributes and the relatively small number of instances. This paper studies this problem in the context of a dataset, from prior work, used to discover promising small molecules for controlling circadian rhythm in humans. By identifying a suitable small subset of the attributes that are effective for this classification task, experimental results indicate accuracies that compare very favorably with prior work on the same data.