title: 5-Year Trends in QSAR and its Machine Learning Methods.
abstract: BACKGROUND
  Quantitative Structure-Activity Relationships (QSAR) is a well-established branch of computational chemistry. The presence of QSAR papers is decreasing for the last few years.
  
  
  OBJECTIVE
  To highlight recent trends of QSAR in general and trends of machine learning methods in particular.
  
  
  METHODS
  A bibliometric analysis of articles published in top ten molecular modeling and medicinal chemistry journals was carried out. The bibliometric statistics was collected for papers published in 2009 and 2015 and compared.
  
  
  RESULTS
  During 5-year span studied, the fraction of QSAR studies underwent a twofold decrease. Top journals of both categories became less likely to publish Multiple Linear Regression models and increased the presence of Random forest and Naïve Bayes methods. 3D-QSAR remains the most popular method of studying structure-activity relationships with a slight decrease of its presence in molecular modeling journals but a relative increase in medicinal chemistry.
  
  
  CONCLUSIONS
  The downward QSAR trend might have several reasons: more stringent criteria for QSAR studies acceptance by journals, transformation of QSAR studies into routine work due to wider availability of QSAR methods and the overall maturation of QSAR field, and possible disappointment in QSAR. We expect that the progress in machine learning methods being adopted by chem(o)informaticians finally will help QSAR to find its place in drug design and to move to the Plateau of Productivity.
authors.forename: Oleg T
authors.initials: OT
authors.lastname: Devinyak
authors.affiliation: Department of Pharmaceutical, Organic and Bioorganic Chemistry, Danylo Halytsky Lviv National Medical University, P.O. Box: 79010, Lviv, Ukraine. dr_r_lesyk@org.lviv.net.
authors.forename: Roman B
authors.initials: RB
authors.lastname: Lesyk
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D015706:Bibliometrics; D003198:Computer Simulation / Q000639:trends; D004059:Diffusion of Innovation; D006801:Humans; D016014:Linear Models; D000069550:Machine Learning / Q000639:trends*; D008958:Models, Molecular; D010506:Periodicals as Topic / Q000639:trends*; D021281:Quantitative Structure-Activity Relationship; D013997:Time Factors