APPLICATION OF GENETIC ALGORITHMS TO COMBINATORIAL SYNTHESIS - A COMPUTATIONAL APPROACH TO LEAD IDENTIFICATION AND LEAD OPTIMIZATION

A genetic algorithms (GA) based strategy is described for the identifi cation or optimization of active leads. This approach does not require the synthesis and evaluation of huge libraries. Instead it involves i terative generations of smaller sample sets, which are assayed, and th e ''experimentally'' determined biological response is used as an inpu t for GA to rapidly find better leads. The GA described here has been applied to the identification of potent and selective stromelysin subs trates from a combinatorial-based population of 20(6) or 64 000 000 po ssible hexapeptides. Using GA, we have synthesized less then 300 uniqu e immobilized peptides in a total of five generations to achieve this end. The results show that each successive generation provided better and unique substrates. An additional strategy of utilizing the knowled ge gained in each generation in a spin-off SAR activity is described h ere. Sequences from the first generations were evaluated for stromelys in and collagenase activity to identify stromelysin-selective substrat es. GlyProSerThr-TyrThr with Tyr as the P1' residue is such an example . A number of peptides replacing Tyr with unusual monomers were synthe sized and evaluated as stromelysin substrates. This led to the identif ication of Ser(OBn) as the best and most selective P1' residue for str omelysin.