Genetic optimization of combinatorial libraries

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Abstract
Most agrochemical and pharmaceutical companies have set up high‐throughput screening programs which require large numbers of compounds to screen. Combinatorial libraries provide an attractive way to deliver these compounds. A single combinatorial library with four variable positions can yield more than 1012 potential compounds, if one assumes that about 1000 reagents are available for each position. This is far more than any high‐throughput screening facility can afford to screen. We have proposed a method for iterative compound selection from large databases, which identifies the most active compounds by examining only a small fraction of the database. In this article, we describe the extension of this method to the problem of selecting compounds from large combinatorial libraries. © 1998 John Wiley & Sons, Inc. Biotechnol Bioeng (Comb Chem) 61:47–54, 1998.