虚拟筛选
计算机科学
工作流程
化学数据库
化学空间
限制
数据挖掘
标杆管理
集成学习
人工智能
高通量筛选
匹配(统计)
药物发现
分类器(UML)
最近邻搜索
对接(动物)
渲染(计算机图形)
吞吐量
覆盖
化学图书馆
公制(单位)
相似性(几何)
Boosting(机器学习)
封面(代数)
极限(数学)
作者
Kirill Shmilovich,Patricia Suriana,Vishnu Sresht
标识
DOI:10.1021/acs.jcim.6c00591
摘要
Ligand-based virtual screening with ROCS (Rapid Overlay of Chemical Structures) enables rapid exploration of billion-member chemical libraries but requires a known active compound to serve as the reference query, limiting application to targets with established chemical matter and biasing results toward close analogs. Structure-based docking approaches are an alternative technique less constrained by prior chemical matter, but substantial computational costs of these methods limit large-scale deployment. Here we present Struct2Query, a workflow that bridges these approaches by converting protein pockets into composite-molecule ROCS queries. Our method leverages OpenEye SiteHopper to efficiently search a curated database of over 78,000 crystallographic protein-ligand complexes from the RCSB, identifying structurally analogous pockets, then transplants ligands from these related pockets to generate an ensemble of binding hypotheses. Rather than consolidating this ligand ensemble into a consensus pharmacophore, we retain all constituent shape and color features in a composite-molecule ROCS query, allowing densely populated regions to emerge as natural hotspots. Benchmarking early enrichment ability on DEKOIS 2.0 (81 targets) and DUDE-Z (43 targets) data sets demonstrates performance matching or exceeding popular structure-based methods such as Glide and HYBRID docking while maintaining the throughput of ligand-centric approaches compatible with GPU-accelerated FastROCS. Scaffold diversity analysis of virtual screening hit lists reveals improved chemotype coverage compared to single-ligand ROCS for three of four purchasable compound libraries tested, with specific considerations for combinatorial libraries discussed. Struct2Query thus enables a structure-informed virtual screening method amenable to the scale and throughput of ligand-based methods.
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