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.British Journal of Pharmacology (2007) 152, 9–20; doi:10.1038/sj.bjp.0707305

Computational (in silico) methods have been developed and widely applied to pharmacology hypothesis development and testing. Thesein silicomethods include databases, quantitative structure‐activity relationships, similarity searching, pharmacophores, homology models and other molecular modeling, machine learning, data mining, network analysis tools and data analysis tools that use a computer. Such methods 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 first part of this review discussed the methods that have been used for virtual ligand and target‐based screening and profiling to predict biological activity. The aim of this second part of the review is to illustrate some of the varied applications ofin silicomethods for pharmacology in terms of the targets addressed. We will also discuss some of the advantages and disadvantages ofin silicomethods with respect toin vitroandin vivomethods for pharmacology research. Our conclusion is that thein silicopharmacology paradigm is ongoing and presents a rich array of opportunities that will assist in expediating the discovery of new targets, and ultimately lead to compounds with predicted biological activity for these novel targets.British Journal of Pharmacology(2007)152, 21–37; doi:10.1038/sj.bjp.0707306