药效团
药物发现
虚拟筛选
ROS1型
计算机科学
化学空间
计算生物学
配体效率
工作流程
组合化学
小分子
化学
生物
配体(生物化学)
癌症
立体化学
生物化学
数据库
腺癌
受体
遗传学
作者
Dušan Petrović,James S. Scott,Michael S. Bodnarchuk,Olivier Lorthioir,Scott Boyd,G. M. Hughes,Jordan Lane,Allan Wu,David Hargreaves,James D. Robinson,Jens Sadowski
标识
DOI:10.1021/acs.jcim.2c00644
摘要
ROS1 rearrangements account for 1–2% of non-small cell lung cancer patients, yet there are no specifically designed, selective ROS1 therapies in the clinic. Previous knowledge of potent ROS1 inhibitors with selectivity over TrkA, a selected antitarget, enabled virtual screening as a hit finding approach in this project. The ligand-based virtual screening was focused on identifying molecules with a similar 3D shape and pharmacophore to the known actives. To that end, we turned to the AstraZeneca virtual library, estimated to cover 1015 synthesizable make-on-demand molecules. We used cloud computing-enabled FastROCS technology to search the enumerated 1010 subset of the full virtual space. A small number of specific libraries were prioritized based on the compound properties and a medicinal chemistry assessment and further enumerated with available building blocks. Following the docking evaluation to the ROS1 structure, the most promising hits were synthesized and tested, resulting in the identification of several potent and selective series. The best among them gave a nanomolar ROS1 inhibitor with over 1000-fold selectivity over TrkA and, from the preliminary established SAR, these have the potential to be further optimized. Our prospective study describes how conceptually simple shape-matching approaches can identify potent and selective compounds by searching ultralarge virtual libraries, demonstrating the applicability of such workflows and their importance in early drug discovery.
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