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CACHE挑战#1:使用GNINA进行对接就足够了。

CACHE Challenge #1: Docking with GNINA Is All You Need.

作者信息

Dunn Ian, Pirhadi Somayeh, Wang Yao, Ravindran Smmrithi, Concepcion Carter, Koes David Ryan

机构信息

Department of Computational and Systems Biology, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, Pennsylvania 15260, United States.

出版信息

J Chem Inf Model. 2024 Dec 23;64(24):9388-9396. doi: 10.1021/acs.jcim.4c01429. Epub 2024 Dec 9.

Abstract

We describe our winning submission to the first Critical Assessment of Computational Hit-Finding Experiments (CACHE) challenge. In this challenge, 23 participants employed a diverse array of structure-based methods to identify hits to a target with no known ligands. We utilized two methods, pharmacophore search and molecular docking, to identify our initial hit list and compounds for the hit expansion phase. Unlike many other participants, we limited ourselves to using docking scores in identifying and ranking hits. Our resulting best hit series tied for first place when evaluated by a panel of expert judges. Here, we report our top-performing open-source workflow and results.

摘要

我们描述了我们在首次计算命中发现实验关键评估(CACHE)挑战赛中的获奖提交内容。在本次挑战赛中,23名参与者采用了各种各样基于结构的方法来识别针对一个没有已知配体的靶点的命中物。我们利用药效团搜索和分子对接这两种方法来确定我们的初始命中物列表以及用于命中物扩展阶段的化合物。与许多其他参与者不同,我们在识别和排名命中物时仅限于使用对接分数。由专家评审团评估时,我们最终得到的最佳命中物系列并列第一名。在此,我们报告我们表现最佳的开源工作流程和结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8044/11683865/95ba5c543fdd/ci4c01429_0001.jpg

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