Discovery Chemistry, Genentech, South San Francisco, California 94080, United States.
J Chem Inf Model. 2024 Feb 26;64(4):1251-1260. doi: 10.1021/acs.jcim.3c01865. Epub 2024 Feb 9.
Virtual screening of large-scale chemical libraries has become increasingly useful for identifying high-quality candidates for drug discovery. While it is possible to exhaustively screen chemical spaces that number on the order of billions, indirect combinatorial approaches are needed to efficiently navigate larger, synthon-based virtual spaces. We describe Shape-Aware Synthon Search (SASS), a synthon-based virtual screening method that carries out shape similarity searches in the synthon space instead of the enumerated product space. SASS can replicate results from exhaustive searches in ultralarge, combinatorial spaces with high recall on a variety of query molecules while only scoring a small subspace of possible enumerated products, thereby significantly accelerating large-scale, shape-based virtual screening.
虚拟筛选大型化学库已成为发现药物的高质量候选物的重要手段。虽然可以彻底筛选数量达到数十亿的化学空间，但需要间接组合方法来有效地导航更大的基于前体的虚拟空间。我们描述了基于形状的前体搜索（Shape-Aware Synthon Search，SASS），这是一种基于前体的虚拟筛选方法，它在前体空间中进行形状相似性搜索，而不是在枚举产物空间中进行。SASS 可以复制在超大组合空间中进行详尽搜索的结果，对各种查询分子具有高召回率，而只对可能枚举产物的一小部分进行评分，从而显著加速大规模基于形状的虚拟筛选。
