Suppr超能文献

阿斯利康基于片段的先导化合物发现的改进模型。

An improved model for fragment-based lead generation at AstraZeneca.

作者信息

Fuller Nathan, Spadola Loredana, Cowen Scott, Patel Joe, Schönherr Heike, Cao Qing, McKenzie Andrew, Edfeldt Fredrik, Rabow Al, Goodnow Robert

机构信息

AstraZeneca, Discovery Sciences, Innovative Medicines and Early Development Biotech Unit, 35 Gatehouse Drive, Waltham, MA 02451, USA.

AstraZeneca, Discovery Sciences, Innovative Medicines and Early Development Biotech Unit, Mereside, Alderley Park, Macclesfield SK10 4TG, UK.

出版信息

Drug Discov Today. 2016 Aug;21(8):1272-83. doi: 10.1016/j.drudis.2016.04.023. Epub 2016 May 11.

Abstract

Modest success rates in fragment-based lead generation (FBLG) projects at AstraZeneca (AZ) prompted operational changes to improve performance. In this review, we summarize these changes, emphasizing the construction and composition of the AZ fragment library, screening practices and working model. We describe the profiles of the screening method for specific fragment subsets and statistically assess our ability to follow up on fragment hits through near-neighbor selection. Performance analysis of our second-generation fragment library (FL2) in screening campaigns illustrates the complementary nature of flat and 3D fragments in exploring protein-binding pockets and highlights our ability to deliver fragment hits using multiple screening techniques for various target classes. The new model has had profound impact on the successful delivery of lead series to drug discovery projects.

摘要

阿斯利康(AZ)基于片段的先导化合物生成(FBLG)项目的成功率一般,促使其进行运营变革以提高绩效。在本综述中,我们总结了这些变革,重点介绍了阿斯利康片段库的构建和组成、筛选方法及工作模式。我们描述了特定片段子集的筛选方法概况,并通过近邻选择对片段命中物进行跟进的能力进行了统计评估。我们在筛选活动中对第二代片段库(FL2)的性能分析表明,扁平片段和三维片段在探索蛋白质结合口袋方面具有互补性,并突出了我们使用多种筛选技术针对不同靶标类别提供片段命中物的能力。新模型对成功向药物发现项目交付先导化合物系列产生了深远影响。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验