title: Quantitative structure-activity relationship: promising advances in drug discovery platforms.
abstract: BACKGROUND
  Quantitative structure-activity relationship (QSAR) modeling is one of the most popular computer-aided tools employed in medicinal chemistry for drug discovery and lead optimization. It is especially powerful in the absence of 3D structures of specific drug targets. QSAR methods have been shown to draw public attention since they were first introduced.
  
  
  METHODS
  In this review, the authors provide a brief discussion of the basic principles of QSAR, model development and model validation. They also highlight the current applications of QSAR in different fields, particularly in virtual screening, rational drug design and multi-target QSAR. Finally, in view of recent controversies, the authors detail the challenges faced by QSAR modeling and the relevant solutions. The aim of this review is to show how QSAR modeling can be applied in novel drug discovery, design and lead optimization.
  
  
  CONCLUSIONS
  QSAR should intentionally be used as a powerful tool for fragment-based drug design platforms in the field of drug discovery and design. Although there have been an increasing number of experimentally determined protein structures in recent years, a great number of protein structures cannot be easily obtained (i.e., membrane transport proteins and G-protein coupled receptors). Fragment-based drug discovery, such as QSAR, could be applied further and have a significant role in dealing with these problems. Moreover, along with the development of computer software and hardware, it is believed that QSAR will be increasingly important.
authors.affiliation: a Zhejiang University, Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Department of Chemical and Biological Engineering , Hangzhou 310027, China +86 571 8795 3080 ; +86 571 8795 3080 ; wumb@zju.edu.cn , linjp@zju.edu.cn.
authors.forename: Tao
authors.initials: T
authors.lastname: Wang
authors.affiliation: a Zhejiang University, Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Department of Chemical and Biological Engineering , Hangzhou 310027, China +86 571 8795 3080 ; +86 571 8795 3080 ; wumb@zju.edu.cn , linjp@zju.edu.cn.
authors.forename: Mian-Bin
authors.initials: MB
authors.lastname: Wu
authors.forename: Jian-Ping
authors.initials: JP
authors.lastname: Lin
authors.affiliation: a Zhejiang University, Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Department of Chemical and Biological Engineering , Hangzhou 310027, China +86 571 8795 3080 ; +86 571 8795 3080 ; wumb@zju.edu.cn , linjp@zju.edu.cn.
authors.forename: Li-Rong
authors.initials: LR
authors.lastname: Yang
mesh_terms: D003198:Computer Simulation; D017076:Computer-Aided Design; D015195:Drug Design; D055808:Drug Discovery / Q000379:methods*; D006801:Humans; D008958:Models, Molecular; D058990:Molecular Targeted Therapy; D021281:Quantitative Structure-Activity Relationship; D012984:Software
keywords: comparative molecular field analysis; comparative molecular similarity indices analysis; machine learning; quantitative structure–activity relationship; rational design; virtual screening