title: High-throughput electronic biology: mining information for drug discovery.
abstract: The vast range of in silico resources that are available in life sciences research hold much promise towards aiding the drug discovery process. To fully realize this opportunity, computational scientists must consider the practical issues of data integration and identify how best to apply these resources scientifically. In this article we describe in silico approaches that are driven towards the identification of testable laboratory hypotheses; we also address common challenges in the field. We focus on flexible, high-throughput techniques, which may be initiated independently of 'wet-lab' experimentation, and which may be applied to multiple disease areas. The utility of these approaches in drug discovery highlights the contribution that in silico techniques can make and emphasizes the need for collaboration between the areas of disease research and computational science.
authors.affiliation: Computational Biology Group, Pfizer, Groton, Connecticut, USA. william.t.loging@pfizer.com
authors.forename: William
authors.initials: W
authors.lastname: Loging
authors.forename: Lee
authors.initials: L
authors.lastname: Harland
authors.forename: Bryn
authors.initials: B
authors.lastname: Williams-Jones
mesh_terms: D000818:Animals; D015195:Drug Design; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D020407:Internet; D008962:Models, Theoretical; D015394:Molecular Structure; D004364:Pharmaceutical Preparations / Q000737:chemistry; D049490:Systems Biology / Q000379:methods*
chemical_list: D004364:Pharmaceutical Preparations