1. ¹ACT LLC, New York, NY, USA
2. ²Department of Pharmaceutical Sciences, University of Maryland, Baltimore, MD, USA
3. ³Chemogenomics Laboratory, Research Unit on Biomedical Informatics, Institut Municipal d'Investigació Mèdica and Universitat Pompeu Fabra, Parc de Recerca Biomèdica, Barcelona, Spain
4. ⁴University Hospital Centre, Lausanne, Switzerland

Correspondence: Dr S Ekins, ACT LLC, 1 Penn Plaza-36th Floor, New York, NY 10119, USA. E-mail: ekinssean@yahoo.com, sekins@arnoldllc.com

Received 21 December 2006; Revised 27 February 2007; Accepted 25 April 2007; Published online 4 June 2007.

Abstract

Pharmacology over the past 100 years has had a rich tradition of scientists with the ability to form qualitative or semi-quantitative relations between molecular structure and activity in cerebro. To test these hypotheses they have consistently used traditional pharmacology tools such as in vivo and in vitro models. Increasingly over the last decade however we have seen that computational (in silico) methods have been developed and applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, pharmacophores, homology models and other molecular modeling approaches, machine learning, data mining, network analysis tools and data analysis tools that use a computer. In silico methods are primarily used alongside the generation of in vitro data both to create the model and to test it. Such models have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The aim of this review is to illustrate some of the in silico methods for pharmacology that are used in drug discovery. Further applications of these methods to specific targets and their limitations will be discussed in the second accompanying part of this review.