Filter Feature Selection for Unsupervised Clustering of Designer Drugs Using DFT Simulated IR Spectra Data

The rapid emergence of novel psychoactive substances (NPS) poses new challenges and requirements for forensic testing/analysis techniques. This paper aims to explore the application of unsupervised clustering of NPS compounds' infrared spectra. Two statistical measures, Pearson and Spearman, were used to quantify the spectral similarity and to generate similarity matrices for hierarchical clustering. The correspondence of spectral similarity clustering trees to the commonly used structural/pharmacological categorization was evaluated and compared to the clustering generated using 2D/3D molecular fingerprints. Hybrid model feature selections were applied using different filter-based feature ranking algorithms developed for unsupervised clustering tasks. Since Spearman tends to overestimate the spectral similarity based on the overall pattern of the full spectrum, the clustering result shows the highest degree of improvement from having the nondiscriminative features removed. The loading plots of the first two principal components of the optimal feature subsets confirmed that the most important vibrational bands contributing to the clustering of NPS compounds were selected using non-negative discriminative feature selection (NDFS) algorithms.