Coping with Polypharmacology by Computational Medicinal Chemistry
Keywords:De novo design, Drug discovery, Machine learning, Molecular informatics, Self-organizing map
AbstractPredicting 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.
How to Cite
G. Schneider, D. Reker, T. Rodrigues, P. Schneider, Chimia 2014, 68, 648, DOI: 10.2533/chimia.2014.648.
Copyright (c) 2014 Swiss Chemical Society
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