Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland.
Novartis Institutes for BioMedical Research Inc., 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.
J Med Chem. 2020 Dec 10;63(23):14425-14447. doi: 10.1021/acs.jmedchem.0c01332. Epub 2020 Nov 3.
This article summarizes the evolution of the screening deck at the Novartis Institutes for BioMedical Research (NIBR). Historically, the screening deck was an assembly of all available compounds. In 2015, we designed a first deck to facilitate access to diverse subsets with optimized properties. We allocated the compounds as plated subsets on a 2D grid with property based ranking in one dimension and increasing structural redundancy in the other. The learnings from the 2015 screening deck were applied to the design of a next generation in 2019. We found that using traditional leadlikeness criteria (mainly MW, clogP) reduces the hit rates of attractive chemical starting points in subset screening. Consequently, the 2019 deck relies on solubility and permeability to select preferred compounds. The 2019 design also uses NIBR's experimental assay data and inferred biological activity profiles in addition to structural diversity to define redundancy across the compound sets.
本文总结了诺华生物医学研究所(NIBR)筛选库的演变。历史上,筛选库是所有可用化合物的集合。2015 年,我们设计了第一个库,以方便访问具有优化特性的各种亚库。我们将化合物分配在二维网格的平板亚库中,在一维中根据性质进行排名,并在另一维中增加结构冗余度。2015 年筛选库的经验教训应用于 2019 年的下一代设计中。我们发现,使用传统的类药性标准(主要是分子量、脂水分配系数)会降低亚库筛选中有吸引力的化学起点的命中率。因此,2019 年的库依赖于溶解度和渗透性来选择首选化合物。2019 年的设计还使用了 NIBR 的实验测定数据和推断的生物活性谱,以及结构多样性,来定义化合物集之间的冗余度。