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大数据时代基于配体的虚拟筛选网络工具和筛选算法在大型分子数据库中的研究进展。

A review of ligand-based virtual screening web tools and screening algorithms in large molecular databases in the age of big data.

机构信息

Bioinformatics & High Performance Computing Research Group (BIO-HPC), Computer Engineering Department. Universidad Católica San Antonio de Murcia (UCAM). Campus de los Jerónimos, 30107, Murcia, Spain.

出版信息

Future Med Chem. 2018 Nov;10(22):2641-2658. doi: 10.4155/fmc-2018-0076. Epub 2018 Nov 30.

Abstract

Virtual screening has become a widely used technique for helping in drug discovery processes. The key to this success is its ability to aid in the identification of novel bioactive compounds by screening large molecular databases. Several web servers have emerged in the last few years supplying platforms to guide users in screening publicly accessible chemical databases in a reasonable time. In this review, we discuss a representative set of online virtual screening servers and their underlying similarity algorithms. Other related topics, such as molecular representation or freely accessible databases are also treated. The most relevant contributions to this review arise from critical discussions concerning the pros and cons of servers and algorithms, and the challenges that future works must solve in a virtual screening framework.

摘要

虚拟筛选已经成为药物发现过程中广泛使用的技术。其成功的关键在于它能够通过筛选大型分子数据库来帮助识别新的生物活性化合物。在过去的几年中,已经出现了几个网络服务器,为用户在合理的时间内筛选公共可访问的化学数据库提供了平台。在这篇综述中,我们讨论了一组有代表性的在线虚拟筛选服务器及其底层相似性算法。还讨论了其他相关主题,如分子表示或免费访问的数据库。这篇综述的最重要贡献来自于对服务器和算法的优缺点以及虚拟筛选框架中未来工作必须解决的挑战的批判性讨论。

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