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Macromolecular target prediction by self-organizing feature maps.

Gisbert Schneider1, Petra Schneider1,2

  • 1a Department of Chemistry and Applied Biosciences , Swiss Federal Institute of Technology (ETH) , Zurich , Switzerland.

Expert Opinion on Drug Discovery
|December 21, 2016
PubMed
Summary

Computational target prediction models, like the self-organizing map (SOM) algorithm, aid drug discovery by identifying compound activities against macromolecular targets. This approach enhances screening, design, and repurposing, proving effective in real-world applications.

Keywords:
Chemical biologydeep learningdrug designmachine learningmedicinal chemistryneural networkoff-targetphenotypic screeningpolypharmacology

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Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Pharmacology

Background:

  • Rational drug discovery requires understanding compound activity against diverse macromolecular targets.
  • Computational target-prediction models aid in various drug discovery stages, including screening, design, and repurposing.
  • The self-organizing map (SOM) algorithm is a successful computational method for these purposes.

Purpose of the Study:

  • To review contemporary artificial neural network methods for macromolecular target prediction.
  • To present the basic SOM algorithm conceptually.
  • To highlight consensus target-scoring using multiple SOMs and discuss its opportunities and limitations.

Main Methods:

  • Review of artificial neural network methods.
  • Conceptual explanation of the self-organizing map (SOM) algorithm.
  • Application of consensus target-scoring with multiple SOMs.

Main Results:

  • Self-organizing feature maps offer a simple yet broadly applicable method for ligand clustering and classification.
  • Consensus scoring using multiple SOMs enhances target prediction accuracy.
  • The SOM approach has demonstrated high success rates in prospective settings.

Conclusions:

  • Self-organizing feature maps are a valuable tool for in silico target identification.
  • Despite limitations, SOMs represent a prototypic technique for advancing drug discovery.
  • This method aids in identifying modes of action and macromolecular targets of bioactive molecules.