虚拟筛选
化学空间
计算机科学
软件
化学信息学
集合(抽象数据类型)
化学数据库
下部结构
相似性(几何)
情报检索
空格(标点符号)
指纹(计算)
化学图书馆
数据挖掘
药物发现
生物信息学
化学
小分子
人工智能
程序设计语言
操作系统
图像(数学)
工程类
生物
结构工程
生物化学
作者
Tuomo Kalliokoski,Ainoleena Turku,Heikki Käsnänen
标识
DOI:10.1021/acs.jcim.4c01790
摘要
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.
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