Simulated molecular evolution in a full combinatorial library

Background: The Darwinian concept of 'survival of the fittest' has inspired the development of evolutionary optimization methods to find molecules wit h desired properties in iterative feedback cycles of synthesis and testing. These methods have recently been applied to the computer-guided heuristic selection of molecules that bind with high affinity to a given biological t arget. We describe the optimization behavior and performance of genetic alg orithms (GAs) that select molecules from a combinatorial library of potenti al thrombin inhibitors in 'artificial molecular evolution' experiments, on the basis of biological screening results. Results: A full combinatorial library of 15,360 members structurally biased towards the serine protease thrombin was synthesized, and all were tested for their ability to inhibit the protease activity of thrombin. Using the r esulting large structure-activity landscape, we simulated the evolutionary selection of potent thrombin inhibitors from this library using GAs. Optima l parameter sets were found (encoding strategy, population size, mutation a nd cross-over rate) for this artificial molecular evolution. Conclusions: A GA-based evolutionary selection is a valuable combinatorial optimization strategy to discover compounds with desired properties without needing to synthesize and test all possible combinations (i.e. all molecul es). GAs are especially powerful when dealing with very large combinatorial libraries for which synthesis and screening of all members is not possible and/or when only a small number of compounds compared with the library siz e can be synthesized or tested. The optimization gradient or 'learning' per individual increases when using smaller population sizes and decreases for higher mutation rates.