The successful discovery of novel biological therapeutics by selection requires highly diverse libraries of candidate sequences that contain a high proportion of desirable candidates. Here we propose the use of computationally designed factorizable libraries made of concatenated segment libraries as a method of creating large libraries that meet an objective function at low cost. We show that factorizable libraries can be designed efficiently by representing objective functions that describe sequence optimality as an inner product of feature vectors, which we use to design an optimization method we call Stochastically Annealed Product Spaces (SAPS). We then use this approach to design diverse and efficient libraries of antibody CDR-H3 sequences with various optimized characteristics.

中文翻译：

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用于有效治疗发现的超高多样性因式分解库

通过选择成功发现新的生物疗法需要高度多样化的候选序列库，其中包含高比例的理想候选序列。在这里，我们建议使用由连接段库组成的计算设计的可分解库，作为一种以低成本创建满足目标函数的大型库的方法。我们表明，可以通过将描述序列最优性的目标函数表示为特征向量的内积来有效地设计可分解库，我们用它来设计一种我们称为随机退火乘积空间 (SAPS) 的优化方法。然后，我们使用这种方法来设计具有各种优化特性的抗体 CDR-H3 序列的多样化和高效文库。