Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus.

TitleIdentifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus.
Publication TypeJournal Article
Year of Publication2014
AuthorsD Reker, T Rodrigues, P Schneider, and G Schneider
JournalProceedings of the National Academy of Sciences of the United States of America
Volume111
Start Page4067
Issue11
Pagination4067 - 4072
Date Published03/2014
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.

DOI10.1073/pnas.1320001111
Short TitleProceedings of the National Academy of Sciences of the United States of America