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

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Reker, D; Rodrigues, T; Schneider, P; Schneider, G

Published Date

  • March 3, 2014

Published In

Volume / Issue

  • 111 / 11

Start / End Page

  • 4067 - 4072

PubMed ID

  • 24591595

Pubmed Central ID

  • PMC3964060

Electronic International Standard Serial Number (EISSN)

  • 1091-6490

International Standard Serial Number (ISSN)

  • 0027-8424

Digital Object Identifier (DOI)

  • 10.1073/pnas.1320001111

Language

  • eng