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MoleQCage:用于分子笼预测的几何高通量筛选

MoleQCage: Geometric High-Throughput Screening for Molecular Caging Prediction.

作者信息

Kravberg Alexander, Devaurs Didier, Varava Anastasiia, Kavraki Lydia E, Kragic Danica

机构信息

School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm 10044, Sweden.

Department of Computer and Information Sciences, University of Strathclyde, Glasgow G1 1XH, United Kingdom.

出版信息

J Chem Inf Model. 2024 Dec 23;64(24):9034-9039. doi: 10.1021/acs.jcim.4c01419. Epub 2024 Dec 12.

Abstract

Although being able to determine whether a host molecule can enclose a guest molecule and form a caging complex could benefit numerous chemical and medical applications, the experimental discovery of molecular caging complexes has not yet been achieved at scale. Here, we propose MoleQCage, a simple tool for the high-throughput screening of host and guest candidates based on an efficient robotics-inspired geometric algorithm for molecular caging prediction, providing theoretical guarantees and robustness assessment. MoleQCage is distributed as Linux-based software with a graphical user interface and is available online at https://hub.docker.com/r/dantrigne/moleqcage in the form of a Docker container. Documentation and examples are available as Supporting Information and online at https://hub.docker.com/r/dantrigne/moleqcage.

摘要

尽管能够确定宿主分子是否可以包裹客体分子并形成笼状复合物将有利于众多化学和医学应用,但分子笼状复合物的实验发现尚未大规模实现。在此,我们提出了MoleQCage,这是一种基于高效的受机器人启发的几何算法进行分子笼状预测的简单工具,用于高通量筛选宿主和客体候选物,提供理论保证和稳健性评估。MoleQCage作为基于Linux的软件发布,带有图形用户界面,可在https://hub.docker.com/r/dantrigne/moleqcage以Docker容器的形式在线获取。文档和示例可作为补充信息在线获取,网址为https://hub.docker.com/r/dantrigne/moleqcage。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8be/11684736/d93f3b1bc9da/ci4c01419_0001.jpg

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