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Many biological processes involve, at some point, the specific binding a protein to some target molecule. The binding might constitute part of a signalling mechanism between cells, it might be part of a mechanical operation such as muscle contraction, or it might mediate a catalytic event, or it might be part of yet another process. One way that drugs can work is competetive inhibition : binding to proteins more strongly than their natural binding partners, and thereby interrupting whatever process the protein mediates.
As an example, consider non-steroidal anti-inflammatory drugs (NSAIDs) such as ibuprofen. These drugs act on a class of proteins called cyclooxygenases (COX), which are involved in the synthesis of chemicals called prostaglandins, which in turn cause pain and inflammation. Inhibition of COX can reduce pain, inflammation, and swelling by substantially reducing the amount of prostaglandins that can be produced. NSAIDs generally work by binding to the active site of COX and blocking it (aspirin and other salicylates are an exception--they disable COX by modifying it chemically).
NSAIDs also illustrate one thing that can go wrong with drugs: side effects. There are actually three classes of COX: COX-1, COX-2, and COX-3. Of the three, COX-2 is the one associated with immune responses, inflammation, and abnormal pain. COX-1 is present in all mammalian cells, as some baseline COX activity is normal. Excessive inhibition of COX in humans is associated with stomach ulcers and indigestion. The problem is one of specificity: In many cases, it is sufficient to inhibit only COX-2 to treat pain and inflammation. In fact, there is a class of NSAIDs called COX-2 inhibitors that do precisely that. In other cases, side effects can be far more severe and dangerous.
Laboratory techniques for drug discovery are very time-consuming and expensive. Each candidate drug must be synthesized and assayed for activity on the target protein, as well as cross-reactivity with non-targets. There is therefore a great deal of interest in developing computational techniques to assist with this stage of drug development. Although they are still largely an area of research rather than production, a number of automated methods have emerged for identifying promising drug candidates. These methods generally fall into one of two categories:
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