Application of Genetic Algorithms to Combinatorial Synthesis: A Computational Approach to Lead Identification and Lead Optimization

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• 作者：Jasbir Singh ; Mark A. Ator ; Edward P. Jaeger ; Martin P. Allen ; David A. Whipple ; James E. Soloweij ; Swapan Chowdhary ; and Adi M. Treasurywala
• 刊名：Journal of the American Chemical Society
• 出版年：1996
• 出版时间：February 21, 1996
• 年：1996
• 卷：118
• 期：7
• 页码：1669 - 1676
• 全文大小：450K
• 年卷期：v.118,no.7(February 21, 1996)
• ISSN：1520-5126

文摘

A genetic algorithms (GA) based strategy is described for theidentification or optimization of activeleads. This approach does not require the synthesis and evaluationof huge libraries. Instead it involves iterativegenerations of smaller sample sets, which are assayed, and the"experimentally" determined biological response isused as an input for GA to rapidly find better leads. The GAdescribed here has been applied to the identificationof potent and selective stromelysin substrates from acombinatorial-based population of 20⁶ or 64 000 000possiblehexapeptides. Using GA, we have synthesized less then 300 uniqueimmobilized peptides in a total of five generationsto achieve this end. The results show that each successivegeneration provided better and unique substrates. Anadditional strategy of utilizing the knowledge gained in eachgeneration in a spin-off SAR activity is described here.Sequences from the first generations were evaluated forstromelysin and collagenase activity to identifystromelysin-selective substrates. GlyProSerThr-TyrThr with Tyr as theP₁' residue is such an example. A number ofpeptidesreplacing Tyr with unusual monomers were synthesized and evaluated asstromelysin substrates. This led to theidentification of Ser(OBn) as the best and most selectiveP₁' residue for stromelysin.