The growth of make-on-demand libraries in recent years has provided completely new possibilities for virtual screening for discovering new hit compounds with specific and favorable properties. However, since these libraries now contain billions of compounds, screening them using traditional methods such as molecular docking has become challenging and requires substantial computational resources. Thus, to take real advantage of the new possibilities introduced by the make-on-demand libraries, different methods have been proposed to accelerate the screening process and prioritize molecules for evaluation. Here, we introduce SpaceGA, a genetic algorithm that leverages the rapid similarity search tool SpaceLight (Bellmann, L.; Penner, P.; Rarey, M. Topological similarity search in large combinatorial fragment spaces. J. Chem. Inf. Model. 2021, 61, 238–251). to constrain the optimization process to accessible compounds within desired combinatorial libraries. As shown herein, SpaceGA is able to efficiently identify molecules with desired properties from trillions of synthesizable compounds by enumerating and evaluating only a small fraction of them. On this basis, SpaceGA represents a promising new tool for accelerating and simplifying virtual screens of ultralarge combinatorial databases.

中文翻译：

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SpaceGA 简介：一种加速组合文库大型虚拟筛选的搜索工具


近年来，按需构建文库的增长为虚拟筛选提供了全新的可能性，以发现具有特定和有利特性的新命中化合物。然而，由于这些文库现在包含数十亿种化合物，因此使用分子对接等传统方法对其进行筛选变得具有挑战性，并且需要大量的计算资源。因此，为了真正利用按需构建文库引入的新可能性，已经提出了不同的方法来加速筛选过程并确定分子的评估优先级。在这里，我们介绍了 SpaceGA，这是一种利用快速相似性搜索工具 SpaceLight （Bellmann， L.;彭纳，P.;Rarey， M. 大型组合片段空间中的拓扑相似性搜索。J. Chem. Inf. 模型。2021， 61， 238–251）。将优化过程限制为所需组合文库中可访问的化合物。如本文所示，SpaceGA 能够通过仅列举和评估其中的一小部分，从数万亿种可合成化合物中有效地识别具有所需特性的分子。在此基础上，SpaceGA 代表了一个很有前途的新工具，用于加速和简化超大型组合数据库的虚拟筛选。