Evaluation of FCV and FCM clustering algorithms in cluster-based compound selection

Abstract

In the last few years, a number of available screening compounds has been growing rapidly due to the recent developments of high-throughput screening in drug discovery. Chemical vendors provide millions of compounds for drug lead identification; however, these compounds are highly redundant. Clustering method that groups similar compounds into families, can be used to analyze such redundancy. One of most used clustering method is cluster-based compound selection, which involves subdividing a set of compounds into clusters and choosing one compound or a small number of compounds from each cluster. However, little research has been done on overlapping method fuzzy c-means (FCM) and fuzzy c-varieties (FCV) clustering algorithms in compound selection research. Therefore, these two clustering algorithms are implemented and the performance is analyzed based on the effectiveness of the clustering results in terms of mean intercluster molecular dissimilarity (MIMDS) where these results are compared with one another. The analysis shows that in terms of MIMDS, the FCV is better than FCM because it clearly shown the uniform results compare to FCM clustering algorithm.