Pharmacophore-driven kinase profiling applied to the PKIS2 chemogenomic dataset

Abstract

Abstract We present a data-driven and unsupervised approach for extracting 3D pharmacophore hypotheses, without prior ligand selection, from a chemogenomic kinase dataset (406 kinases and 645 compounds, PKIS2). A metric called NEM for Normalized Enrichment Measure is introduced for each pharmacophore, which quantifies change in the proportion of active compounds consistent with the pharmacophore compared to the original dataset. Based on this metric, we can identify pharmacophores associated with specific kinases and, conversely, determine all kinases that share similar metric values. This approach enables the characterization of polypharmacological profiles linked to individual pharmacophore hypotheses. We further evaluate the consistency of our results with various biological datasets, including ChEMBL, DrugBank, LINCS, KINOMEscan, and Kinobeads, showing agreement across representative case studies. This study highlights the potential of our approach for elucidating relationships between pharmacophores and kinase selectivity profiles, providing a scalable framework for exploring kinase–ligand interaction landscapes.