Methods for Clustering Mass Spectrometry Data in Drug Development

Isolation and purification of the active principle within natural compounds plays an important role in drug development. MS (mass spectrometry) is used as a detector in HPLC (high performance liquid chromatography) systems to aid the determination of novel compound structures. Clustering techniques provide useful tools for intelligent data analysis within this context. In this paper, we analyse some representative clustering algorithms, describe the complexities of the mass spectrometry data generated by HPLC-MS systems, and provide a new algorithm for clustering, based on the needs of drug development. This new algorithm is based on the definition of a dynamic window within the instance space.