title: Identifying targets for drug discovery using bioinformatics.
abstract: BACKGROUND
  Drug discovery is the process of discovering and designing drugs, which includes target identification, target validation, lead identification, lead optimization and introduction of the new drugs to the public. This process is very important, involving analyzing the causes of the diseases and finding ways to tackle them.
  
  
  OBJECTIVE
  The problems we must face include: i) that this process is so long and expensive that it might cost millions of dollars and take a dozen years; and ii) the accuracy of identification of targets is not good enough, which in turn delays the process. Introducing bioinformatics into the drug discovery process could contribute much to it. Bioinformatics is a booming subject combining biology with computer science. It can explore the causes of diseases at the molecular level, explain the phenomena of the diseases from the angle of the gene and make use of computer techniques, such as data mining, machine learning and so on, to decrease the scope of analysis and enhance the accuracy of the results so as to reduce the cost and time.
  
  
  METHODS
  Here we describe recent studies about how to apply bioinformatics techniques in the four phases of drug discovery, how these techniques improve the drug discovery process and some possible difficulties that should be dealt with.
  
  
  RESULTS
  We conclude that combining bioinformatics with drug discovery is a very promising method although it faces many problems currently.
authors.affiliation: Deakin University, Faculty of Science and Technology, Australia. phoebe@deakin.edu.au
authors.forename: Yi-Ping Phoebe
authors.initials: YP
authors.lastname: Chen
authors.forename: Feng
authors.initials: F
authors.lastname: Chen
mesh_terms: D000818:Animals; D019295:Computational Biology / Q000379:methods*; D017076:Computer-Aided Design; D016208:Databases, Factual; D016503:Drug Delivery Systems / Q000379:methods*; D015195:Drug Design; D006801:Humans; D008958:Models, Molecular