Advances and Challenges in Computational Target Prediction

J Chem Inf Model. 2019 May 28;59(5):1728-1742. doi: 10.1021/acs.jcim.8b00832. Epub 2019 Feb 28.


Target deconvolution is a vital initial step in preclinical drug development to determine research focus and strategy. In this respect, computational target prediction is used to identify the most probable targets of an orphan ligand or the most similar targets to a protein under investigation. Applications range from the fundamental analysis of the mode-of-action over polypharmacology or adverse effect predictions to drug repositioning. Here, we provide a review on published ligand- and target-based as well as hybrid approaches for computational target prediction, together with current limitations and future directions.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Animals
  • Drug Design*
  • Drug Discovery / methods*
  • Drug Repositioning / methods
  • Humans
  • Ligands
  • Machine Learning
  • Polypharmacology
  • Protein Interaction Maps / drug effects
  • Proteins / metabolism


  • Ligands
  • Proteins