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SpaceHASTEN:一种用于非枚举虚拟化学库的基于结构的虚拟筛选工具。

SpaceHASTEN: A Structure-Based Virtual Screening Tool for Nonenumerated Virtual Chemical Libraries.

作者信息

Kalliokoski Tuomo, Turku Ainoleena, Käsnänen Heikki

机构信息

Orion Pharma, Orionintie 1A, 02101 Espoo, Finland.

出版信息

J Chem Inf Model. 2025 Jan 13;65(1):125-132. doi: 10.1021/acs.jcim.4c01790. Epub 2024 Dec 22.

DOI:10.1021/acs.jcim.4c01790
PMID:39710946
Abstract

Given the size of the relevant chemical space for drug discovery, working with fully enumerated compound libraries (especially in three-dimensional (3D)) is unfeasible. Nonenumerated virtual chemical spaces are a practical solution to this issue, where compounds are described as building blocks which are then connected by rules. One concrete example of such is the BioSolveIT chemical spaces file format (.space). Tools to search these space-files exist that are using ligand-based methods including two-dimensional (2D) fingerprint similarity, substructure matching, and fuzzier similarity metrics such as FTrees. However, there is no software available that enables the screening of these nonenumerated spaces using protein structure as the input query. Here, a hybrid ligand/structure-based virtual screening tool, called SpaceHASTEN, was developed on top of SpaceLight, FTrees, LigPrep, and Glide to allow efficient structure-based virtual screening of nonenumerated chemical spaces. SpaceHASTEN was validated using three public targets picked from the DUD-E data set. It was able to retrieve a large number of diverse and novel high-scoring compounds (virtual hits) from nonenumerated chemical spaces of billions of molecules, after docking a few million compounds. The software can be freely used and is available from http://github.com/TuomoKalliokoski/SpaceHASTEN.

摘要

鉴于药物发现相关化学空间的规模,使用完全枚举的化合物库(尤其是三维(3D)库)是不可行的。非枚举虚拟化学空间是解决此问题的一种实用方法,其中化合物被描述为构建模块,然后通过规则进行连接。此类的一个具体示例是BioSolveIT化学空间文件格式(.space)。存在使用基于配体的方法搜索这些空间文件的工具,包括二维(2D)指纹相似性、子结构匹配以及诸如FTrees等更模糊的相似性度量。然而,没有可用的软件能够使用蛋白质结构作为输入查询来筛选这些非枚举空间。在此,在SpaceLight、FTrees、LigPrep和Glide的基础上开发了一种名为SpaceHASTEN的基于配体/结构的混合虚拟筛选工具,以允许对非枚举化学空间进行高效的基于结构的虚拟筛选。使用从DUD-E数据集中挑选的三个公共靶点对SpaceHASTEN进行了验证。在对接数百万个化合物后,它能够从数十亿分子的非枚举化学空间中检索出大量多样且新颖的高分化合物(虚拟命中物)。该软件可免费使用,可从http://github.com/TuomoKalliokoski/SpaceHASTEN获取。

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