Brown R D, Martin Y C
Pharmaceutical Products Division, Abbott Laboratories, Abbott Park, Illinois 60064-3500, USA.
J Med Chem. 1997 Jul 18;40(15):2304-13. doi: 10.1021/jm970033y.
The design of combinatorial mixture libraries should take account of a number of factors. This paper describes the application of a genetic algorithm to optimizing the diversity of libraries while minimizing the effort that will be needed to deconvolute the biological hits by mass-spectroscopic techniques. It differs from previous applications of genetic algorithms to combinatorial library design in that each chromosome encodes an entire library with the result that properties of the library are optimized. Our method is such that it is easily extensible to optimizing the distributions of any number of physical or other properties of the library. The method allows for the combinatorial constraint inherent in mixtures that every substituent at each diversity site must occur in combination with every substituent at every other site. We present results showing that the genetic algorithm can produce good library designs in timely manner.
组合混合物文库的设计应考虑诸多因素。本文描述了一种遗传算法的应用，该算法用于优化文库的多样性，同时将通过质谱技术对生物活性物质进行反卷积所需的工作量降至最低。它与遗传算法先前在组合文库设计中的应用不同，在于每个染色体对整个文库进行编码，从而优化文库的性质。我们提出的方法易于扩展，可用于优化文库中任意数量物理性质或其他性质的分布。该方法考虑了混合物中固有的组合约束，即每个多样性位点的每个取代基必须与其他每个位点的每个取代基组合出现。我们给出的结果表明，遗传算法能够及时产生良好的文库设计。
