Introduction to Chemoinformatics in Drug Discovery – A Personal View

The first issue to be discussed is the definition of the topic. What is chemoinformatics and why should you care? There is no clear definition, although a consensus view appears to be emerging. ‘‘Chemoinformatics is the mixing of those information resources to transform data into information and information into knowledge for the intended purpose of making better decisions faster in the area of drug lead identification and organization’’ according to one view [1]. Hann and Green suggest that chemoinformatics is simply a new name for an old problem [2], a viewpoint I share. There are sufficient reviews [3–6] and even a book by Leach and Gillet [7] with the topic as their focus that there is little doubt what is meant, despite the absence of a precise definition that is generally accepted. One aspect of a new emphasis is the sheer magnitude of chemical information that must be processed. For example, Chemical Abstracts Service adds over three-quarters of a million new compounds to its database annually, for which large amounts of physical and chemical property data are available. Some groups generate hundreds of thousands to millions of compounds on a regular basis through combinatorial chemistry that are screened for biological activity. Even more compounds are generated and screened in silico in the search for a magic bullet for a given disease. Either one of the two processes for generating information about chemistry has its own limitations. Experimental approaches have practical limitations despite automation; each in vitro bioassay utilizes a finite amount of reagents including valuable cloned and expressed receptors. Computational chemistry has to establish relevant criteria by which to select compounds of interest for synthesis and testing. The accuracy of prediction of affinities with current methodology is just now approaching sufficient accuracy to be of utility. Let me emphasize the magnitude of the problem with a simple example. I was once asked to estimate the number of compounds covered by a typical issued patent for a drug of commercial interest. The patent that I selected to analyze was for enalapril, a prominent prodrug ACE inhibitor with a well-established commercial market. Given the parameters as outlined in the patent covering enalapril, an estimation of the total number of compounds included in the generic claim for enalaprilat, the active