Suppr超能文献

一个开源药物发现平台可实现超大规模虚拟筛选。

An open-source drug discovery platform enables ultra-large virtual screens.

机构信息

Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Harvard University, Boston, MA, USA.

Department of Physics, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, USA.

出版信息

Nature. 2020 Apr;580(7805):663-668. doi: 10.1038/s41586-020-2117-z. Epub 2020 Mar 9.

Abstract

On average, an approved drug currently costs US$2-3 billion and takes more than 10 years to develop. In part, this is due to expensive and time-consuming wet-laboratory experiments, poor initial hit compounds and the high attrition rates in the (pre-)clinical phases. Structure-based virtual screening has the potential to mitigate these problems. With structure-based virtual screening, the quality of the hits improves with the number of compounds screened. However, despite the fact that large databases of compounds exist, the ability to carry out large-scale structure-based virtual screening on computer clusters in an accessible, efficient and flexible manner has remained difficult. Here we describe VirtualFlow, a highly automated and versatile open-source platform with perfect scaling behaviour that is able to prepare and efficiently screen ultra-large libraries of compounds. VirtualFlow is able to use a variety of the most powerful docking programs. Using VirtualFlow, we prepared one of the largest and freely available ready-to-dock ligand libraries, with more than 1.4 billion commercially available molecules. To demonstrate the power of VirtualFlow, we screened more than 1 billion compounds and identified a set of structurally diverse molecules that bind to KEAP1 with submicromolar affinity. One of the lead inhibitors (iKeap1) engages KEAP1 with nanomolar affinity (dissociation constant (K) = 114 nM) and disrupts the interaction between KEAP1 and the transcription factor NRF2. This illustrates the potential of VirtualFlow to access vast regions of the chemical space and identify molecules that bind with high affinity to target proteins.

摘要

平均而言,一种已获批准的药物目前的成本为 20 亿至 30 亿美元,开发时间超过 10 年。部分原因是昂贵且耗时的湿实验室实验、初始命中化合物质量差以及(临床前)阶段的高淘汰率。基于结构的虚拟筛选有可能缓解这些问题。基于结构的虚拟筛选,随着筛选化合物数量的增加,命中质量会提高。然而,尽管存在大量化合物数据库,但以可访问、高效和灵活的方式在计算机集群上进行大规模基于结构的虚拟筛选的能力仍然难以实现。在这里,我们描述了 VirtualFlow,这是一个高度自动化和多功能的开源平台,具有完美的扩展行为,能够准备和高效筛选超大规模的化合物库。VirtualFlow 能够使用各种最强大的对接程序。使用 VirtualFlow,我们准备了一个最大的、免费的可随时对接配体库,其中包含超过 14 亿种商业上可用的分子。为了展示 VirtualFlow 的强大功能,我们筛选了超过 10 亿种化合物,并鉴定出了一组与 KEAP1 具有亚毫摩尔亲和力的结构多样的分子。其中一种先导抑制剂 (iKeap1) 以纳摩尔亲和力(解离常数 (K) = 114 nM)与 KEAP1 结合,并破坏 KEAP1 与转录因子 NRF2 之间的相互作用。这说明了 VirtualFlow 可以访问广阔的化学空间,并鉴定出与靶蛋白具有高亲和力结合的分子的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d957/8352709/b3b9e5b8f838/nihms-1568258-f0004.jpg

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