Coping with Polypharmacology by Computational Medicinal Chemistry

Authors

  • Gisbert Schneider Eidgenössische Technische Hochschule, Department of Chemistry and Applied Biosciences Computer-Assisted Drug Design, Vladimir-Prelog-Weg 4, CH-8093 Zürich, Switzerland. gisbert.schneider@pharma.ethz.ch
  • Daniel Reker Eidgenössische Technische Hochschule, Department of Chemistry and Applied Biosciences Computer-Assisted Drug Design, Vladimir-Prelog-Weg 4, CH-8093 Zürich, Switzerland
  • Tiago Rodrigues Eidgenössische Technische Hochschule, Department of Chemistry and Applied Biosciences Computer-Assisted Drug Design, Vladimir-Prelog-Weg 4, CH-8093 Zürich, Switzerland
  • Petra Schneider Eidgenössische Technische Hochschule, Department of Chemistry and Applied Biosciences Computer-Assisted Drug Design, Vladimir-Prelog-Weg 4, CH-8093 Zürich, Switzerland

DOI:

https://doi.org/10.2533/chimia.2014.648

Keywords:

De novo design, Drug discovery, Machine learning, Molecular informatics, Self-organizing map

Abstract

Predicting the macromolecular targets of drug-like molecules has become everyday practice in medicinal chemistry. We present an overview of our recent research activities in the area of polypharmacology-guided drug design. A focus is put on the self-organizing map (SOM) as a tool for compound clustering and visualization. We show how the SOM can be efficiently used for target-panel prediction, drug re-purposing, and the design of focused compound libraries. We also present the concept of virtual organic synthesis in combination with quantitative estimates of ligand-receptor binding, which we used for de novo designing target-selective ligands. We expect these and related approaches to enable the future discovery of personalized medicines.

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Published

2014-09-24

How to Cite

[1]
G. Schneider, D. Reker, T. Rodrigues, P. Schneider, Chimia 2014, 68, 648, DOI: 10.2533/chimia.2014.648.