title: Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus.
abstract: De novo molecular design and in silico prediction of polypharmacological profiles are emerging research topics that will profoundly affect the future of drug discovery and chemical biology. The goal is to identify the macromolecular targets of new chemical agents. Although several computational tools for predicting such targets are publicly available, none of these methods was explicitly designed to predict target engagement by de novo-designed molecules. Here we present the development and practical application of a unique technique, self-organizing map-based prediction of drug equivalence relationships (SPiDER), that merges the concepts of self-organizing maps, consensus scoring, and statistical analysis to successfully identify targets for both known drugs and computer-generated molecular scaffolds. We discovered a potential off-target liability of fenofibrate-related compounds, and in a comprehensive prospective application, we identified a multitarget-modulating profile of de novo designed molecules. These results demonstrate that SPiDER may be used to identify innovative compounds in chemical biology and in the early stages of drug discovery, and help investigate the potential side effects of drugs and their repurposing options.
authors.affiliation: Institute of Pharmaceutical Sciences, Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology, 8093 Zurich, Switzerland.
authors.forename: Daniel
authors.initials: D
authors.lastname: Reker
authors.forename: Tiago
authors.initials: T
authors.lastname: Rodrigues
authors.forename: Petra
authors.initials: P
authors.lastname: Schneider
authors.forename: Gisbert
authors.initials: G
authors.lastname: Schneider
mesh_terms: D001185:Artificial Intelligence; D002616:Chemical Engineering / Q000379:methods*; D055808:Drug Discovery / Q000379:methods*; D058492:Drug Repositioning / Q000379:methods*; D046911:Macromolecular Substances / Q000737:chemistry*; D064798:Polypharmacology; D012984:Software
chemical_list: D046911:Macromolecular Substances
keywords: chemical similarity; drug design; machine learning; polypharmacology; target prediction