title: Identification of drug candidates and repurposing opportunities through compound-target interaction networks.
abstract: BACKGROUND
  System-wide identification of both on- and off-targets of chemical probes provides improved understanding of their therapeutic potential and possible adverse effects, thereby accelerating and de-risking drug discovery process. Given the high costs of experimental profiling of the complete target space of drug-like compounds, computational models offer systematic means for guiding these mapping efforts. These models suggest the most potent interactions for further experimental or pre-clinical evaluation both in cell line models and in patient-derived material.
  
  
  METHODS
  The authors focus here on network-based machine learning models and their use in the prediction of novel compound-target interactions both in target-based and phenotype-based drug discovery applications. While currently being used mainly in complementing the experimentally mapped compound-target networks for drug repurposing applications, such as extending the target space of already approved drugs, these network pharmacology approaches may also suggest completely unexpected and novel investigational probes for drug development.
  
  
  CONCLUSIONS
  Although the studies reviewed here have already demonstrated that network-centric modeling approaches have the potential to identify candidate compounds and selective targets in disease networks, many challenges still remain. In particular, these challenges include how to incorporate the cellular context and genetic background into the disease networks to enable more stratified and selective target predictions, as well as how to make the prediction models more realistic for the practical drug discovery and therapeutic applications.
authors.affiliation: a 1 University of Helsinki, Institute for Molecular Medicine Finland FIMM , Helsinki, Finland.|b 2 Aalto University, Helsinki Institute for Information Technology HIIT, Department of Computer Science , Espoo, Finland.
authors.forename: Anna
authors.initials: A
authors.lastname: Cichonska
authors.affiliation: c 3 Aalto University, Helsinki Institute for Information Technology HIIT, Department of Computer Science , Espoo, Finland.
authors.forename: Juho
authors.initials: J
authors.lastname: Rousu
authors.affiliation: d 4 University of Helsinki, Institute for Molecular Medicine Finland FIMM , Helsinki, Finland +358 5 03 18 24 26 ; tero.aittokallio@fimm.fi.|e 5 University of Turku, Department of Mathematics and Statistics , Turku, Finland.
authors.forename: Tero
authors.initials: T
authors.lastname: Aittokallio
mesh_terms: D000818:Animals; D002460:Cell Line; D003198:Computer Simulation; D055808:Drug Discovery / Q000379:methods*; D004353:Drug Evaluation, Preclinical / Q000379:methods; D058492:Drug Repositioning; D006801:Humans; D058990:Molecular Targeted Therapy; D010600:Pharmacology
keywords: cell-based models; drug repositioning; drug–target interactions; machine learning; network pharmacology; phenotypic screening; target validation