A comparison of some hierarchal agglomerative clustering algorithms for structure—property correlation

Six different hierarchal clustering algorithms were used to cluster eleven sets of compounds for which associated property data were available. The effectiveness of the clustering in each case was assessed by inspection of the resulting tree diagram representing the classification and by the utility of the classification for molecular property prediction. The best predictions were generally obtained by using the algorithm proposed by Ward while the widely-used nearest neighbour algorithm performed very badly. Unstandardised data gave better results than standardised data although the difference was less when structurally related, rather than structurally disparate, groups of compounds were tested.     

Calculating the knowledge-based similarity of functional groups using crystallographic data

A knowledge-based method for calculating the similarity of functional groups is described and validated. The method is based on experimental information derived from small molecule crystal structures. These data are used in the form of scatterplots that show the likelihood of a non-bonded interaction being formed between functional group A (the `central group') and functional group B (the `contact group' or `probe'). The scatterplots are converted into three-dimensional maps that show the propensity of the probe at different positions around the central group. Here we describe how to calculate the similarity of a pair of central groups based on these maps. The similarity method is validated using bioisosteric functional group pairs identified in the Bioster database and Relibase. The Bioster database is a critical compilation of thousands of bioisosteric molecule pairs, including drugs, enzyme inhibitors and agrochemicals. Relibase is an object-oriented database containing structural data about protein-ligand interactions. The distributions of the similarities of the bioisosteric functional group pairs are compared with similarities for all the possible pairs in IsoStar, and are found to be significantly different. Enrichment factors are also calculated showing the similarity method is statistically significantly better than random in predicting bioisosteric functional group pairs.