Abstract. This paper concerns the use of fungi in pharmaceutical design. More specifically, this research involves mining a database of fungi to determine which ones have waste products that are unusual in their spectral fingerprints, and therefore worth being tested for medicinal properties. The technique described in this paper involves Minimum Message Length encoding. Minimum Message Length (sometimes called Minimum Description Length) encoding is a method for choosing a binary coding for a set of data. The method's goal is to use the frequency of occurrence of each data point to ensure that frequently occurring data are given short codes. Minimum Message Length encoding provides a solution that is optimal in the sense that if the entire data set is employed in the encoding, then the code generated will have the property that no other unambiguous prefix code will provide a shorter encoded version of the entire set. In this paper, the process is turned on its head. The problem that is addressed is: given a large database, how can we pick out the elements that are quite different from the others. The first step in our solution involves using the Minimum Message Length algorithm to generate a compact code for all, or a representative learning section, of the data. The data that require long descriptions in this code are likely to be the ones that possess unusual features. In this paper, we describe this process in some detail, and explain the application of it to a database of fungi
