Structure-based pharmacophore modeling 2. Developing a novel framework for structure-based pharmacophore model generation and selection
Abstract
Pharmacophore models are three-dimensional arrangements of molecular features required for biological activity that are used in ligand identification efforts for many biological targets, including G protein-coupled receptors (GPCR). Though GPCR are integral membrane proteins of considerable interest as targets for drug development, many of these receptors lack known ligands or experimentally determined structures necessary for ligand- or structure-based pharmacophore model generation, respectively. Thus, we here present a structure-based pharmacophore modeling approach that uses fragments placed with Multiple Copy Simultaneous Search (MCSS) to generate high-performing pharmacophore models in the context of experimentally determined, as well as modeled GPCR structures. Moreover, we have addressed the oft-neglected topic of pharmacophore model selection via development of a cluster-then-predict machine learning workflow. Herein score-based pharmacophore models were generated in experimentally determined and modeled structures of 13 class A GPCR and resulted in pharmacophore models exhibiting high enrichment factors when used to search a database containing 569 class A GPCR ligands. In addition, classification of pharmacophore models with the best performing cluster-then-predict logistic regression classifier resulted in positive predictive values (PPV) of 0.88 and 0.76 for selecting high enrichment pharmacophore models from among those generated in experimentally determined and modeled structures, respectively.
Publication Title
Journal of Molecular Graphics and Modelling
Recommended Citation
Szwabowski, G., Daigle, B., Baker, D., & Parrill, A. (2023). Structure-based pharmacophore modeling 2. Developing a novel framework for structure-based pharmacophore model generation and selection. Journal of Molecular Graphics and Modelling, 122 https://doi.org/10.1016/j.jmgm.2023.108488
