PT  - JOURNAL ARTICLE
AU  - Reker, Daniel
AU  - Rodrigues, Tiago
AU  - Schneider, Petra
AU  - Schneider, Gisbert
TI  - Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus
AID  - 10.1073/pnas.1320001111
DP  - 2014 Mar 18
TA  - Proceedings of the National Academy of Sciences
PG  - 4067--4072
VI  - 111
IP  - 11
4099  - http://www.pnas.org/content/111/11/4067.short
4100  - http://www.pnas.org/content/111/11/4067.full
SO  - Proc Natl Acad Sci USA2014 Mar 18; 111
AB  - New chemical entities (NCEs) with desired pharmacological and biological activity spectra fuel drug discovery and provide tools for chemical biologists. Computer-assisted molecular design generates novel chemotypes with predictable polypharmacologies. We present the successful application of fully automated de novo drug design coupled with a pioneering approach for target panel prediction to obtain readily synthesizable bioactive compounds. This innovative concept enabled the identification of relevant macromolecular targets of computationally designed NCEs and led to the discovery of previously unknown off-targets of approved drugs.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.