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

Publication ,  Journal Article
Reker, D; Rodrigues, T; Schneider, P; Schneider, G
Published in: Proceedings of the National Academy of Sciences of the United States of America
March 2014

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.

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Published In

Proceedings of the National Academy of Sciences of the United States of America

DOI

EISSN

1091-6490

ISSN

0027-8424

Publication Date

March 2014

Volume

111

Issue

11

Start / End Page

4067 / 4072

Related Subject Headings

  • Software
  • Polypharmacology
  • Macromolecular Substances
  • Drug Repositioning
  • Drug Discovery
  • Chemical Engineering
  • Artificial Intelligence
 

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Reker, D., Rodrigues, T., Schneider, P., & Schneider, G. (2014). Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus. Proceedings of the National Academy of Sciences of the United States of America, 111(11), 4067–4072. https://doi.org/10.1073/pnas.1320001111
Reker, Daniel, Tiago Rodrigues, Petra Schneider, and Gisbert Schneider. “Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus.Proceedings of the National Academy of Sciences of the United States of America 111, no. 11 (March 2014): 4067–72. https://doi.org/10.1073/pnas.1320001111.
Reker D, Rodrigues T, Schneider P, Schneider G. Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus. Proceedings of the National Academy of Sciences of the United States of America. 2014 Mar;111(11):4067–72.
Reker, Daniel, et al. “Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus.Proceedings of the National Academy of Sciences of the United States of America, vol. 111, no. 11, Mar. 2014, pp. 4067–72. Epmc, doi:10.1073/pnas.1320001111.
Reker D, Rodrigues T, Schneider P, Schneider G. Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus. Proceedings of the National Academy of Sciences of the United States of America. 2014 Mar;111(11):4067–4072.
Journal cover image

Published In

Proceedings of the National Academy of Sciences of the United States of America

DOI

EISSN

1091-6490

ISSN

0027-8424

Publication Date

March 2014

Volume

111

Issue

11

Start / End Page

4067 / 4072

Related Subject Headings

  • Software
  • Polypharmacology
  • Macromolecular Substances
  • Drug Repositioning
  • Drug Discovery
  • Chemical Engineering
  • Artificial Intelligence