Freier S M, Konings D A, Wyatt J R, Ecker D J
ISIS Pharmaceuticals, Carlsbad, California 92008.
J Med Chem. 1995 Jan 20;38(2):344-52. doi: 10.1021/jm00002a016.
Iterative synthesis and screening strategies have recently been used to identify unique active molecules from complex synthetic combinatorial libraries. These techniques have many advantages over traditional screening methods, including the potential to screen large numbers of compounds to identify an active molecule while avoiding analytical separations and structural determination of unknown compounds. It is not clear, however, whether these techniques identify the most active molecular species in the mixtures and, if so, how often. Two key factors which may affect success of the selection process are the presence of many active compounds in the library with a range of activities and the chosen order of unrandomization. The importance of these factors has not been previously studied. Moreover, the impact of experimental errors in determination of subset activities or in randomization during library synthesis is not known. We describe here a model system based on oligonucleotide hybridization that addresses these questions using computer simulations. The results suggested that, within achievable experimental and library synthesis error, iterative deconvolution methods generally find either the best molecule or one with activity very close to the best. The presence of many active compounds in a library influenced the profile of subset activities, but did not preclude selection of a molecule with near optimal activity.
迭代合成与筛选策略近来已被用于从复杂的合成组合文库中鉴定独特的活性分子。相较于传统筛选方法，这些技术具有诸多优势，包括有潜力筛选大量化合物以鉴定活性分子，同时避免对未知化合物进行分析分离和结构测定。然而，尚不清楚这些技术能否鉴定混合物中活性最强的分子种类，若能鉴定，其频率如何。可能影响筛选过程成功与否的两个关键因素是文库中存在多种具有不同活性范围的活性化合物以及所选择的解随机化顺序。此前尚未研究过这些因素的重要性。此外，在子集活性测定或文库合成过程中的随机化过程中实验误差的影响尚不清楚。我们在此描述一个基于寡核苷酸杂交的模型系统，该系统利用计算机模拟来解决这些问题。结果表明，在可实现的实验和文库合成误差范围内，迭代去卷积方法通常能找到最佳分子或活性与最佳分子非常接近的分子。文库中存在多种活性化合物会影响子集活性的分布，但并不妨碍选择具有接近最佳活性的分子。
