title: Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework?
abstract: BACKGROUND
  Artificial neural networks (ANNs) are highly adaptive nonlinear optimization algorithms that have been applied in many diverse scientific endeavors, ranging from economics, engineering, physics, and chemistry to medical science. Notably, in the past two decades, ANNs have been used widely in the process of drug discovery.
  
  
  METHODS
  In this review, the authors discuss advantages and disadvantages of ANNs in drug discovery as incorporated into the quantitative structure-activity relationships (QSAR) framework. Furthermore, the authors examine the recent studies, which span over a broad area with various diseases in drug discovery. In addition, the authors attempt to answer the question about the expectations of the ANNs in drug discovery and discuss the trends in this field.
  
  
  CONCLUSIONS
  The old pitfalls of overtraining and interpretability are still present with ANNs. However, despite these pitfalls, the authors believe that ANNs have likely met many of the expectations of researchers and are still considered as excellent tools for nonlinear data modeling in QSAR. It is likely that ANNs will continue to be used in drug development in the future.
authors.affiliation: a Department of Chemistry , Tallinn University of Technology , Tallinn , Estonia.
authors.forename: Dimitar
authors.initials: D
authors.lastname: Dobchev
authors.affiliation: b Institute of Chemistry , University of Tartu , Tartu , Estonia.
authors.forename: Mati
authors.initials: M
authors.lastname: Karelson
mesh_terms: D000465:Algorithms; D000818:Animals; D015195:Drug Design; D055808:Drug Discovery / Q000379:methods*; D006801:Humans; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D004364:Pharmaceutical Preparations / Q000737:chemistry; D021281:Quantitative Structure-Activity Relationship
chemical_list: D004364:Pharmaceutical Preparations
keywords: Artificial neural networks; QSAR; drug discovery