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基于合成子的策略:利用分子相似性和蛋白质-配体相互作用高效筛选超大型化学文库

Synthon-Based Strategies Exploiting Molecular Similarity and Protein-Ligand Interactions for Efficient Screening of Ultra-Large Chemical Libraries.

作者信息

Medel-Lacruz Brian, Herrero Albert, Martín Fernando, Herrero Enric, Luque F Javier, Vázquez Javier

机构信息

Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain and Department of Medicine and Life Sciences, Pompeu Fabra University (UPF), 08003 Barcelona, Spain.

Pharmacelera, Parc Científic de Barcelona (PCB), 08028 Barcelona, Spain.

出版信息

J Chem Inf Model. 2025 Jul 28;65(14):7569-7583. doi: 10.1021/acs.jcim.5c00222. Epub 2025 Apr 28.

Abstract

The rapid expansion of ultralarge chemical libraries has revolutionized drug discovery, providing access to billions of compounds. However, this growth poses relevant challenges for traditional virtual screening (VS) methods. To address these limitations, synthon-based approaches have emerged as scalable alternatives, exploiting combinatorial chemistry principles to prioritize building blocks over enumerated molecules. In this work, we present exaScreen and exaDock, two novel synthon-based methodologies designed for ligand-based and structure-based VS, respectively. In the former case, synthon selection is guided by the 3D hydrophobic/philic distribution pattern in conjunction with a specific synthon alignment protocol based on a quadrupolar expansion over the atoms that participate in the linking bonds between fragments. On the other hand, accommodation to the binding site under a geometrically restrained docking of synthon-based hybrid compounds is used in the selection of the optimal synthon combinations. These strategies exhibit comparable performance to the search performed using fully enumerated libraries in identifying active compounds with significantly lower computational cost, offering computationally efficient strategies for VS in ultralarge chemical spaces.

摘要

超大型化学文库的迅速扩展彻底改变了药物发现过程,使人们能够获取数十亿种化合物。然而,这种增长给传统虚拟筛选(VS)方法带来了相关挑战。为了克服这些限制,基于合成子的方法应运而生,成为可扩展的替代方案,利用组合化学原理将构建模块置于枚举分子之上进行优先考虑。在这项工作中,我们展示了exaScreen和exaDock,这两种新颖的基于合成子的方法分别设计用于基于配体和基于结构的虚拟筛选。在前一种情况下,合成子的选择由三维疏水/亲水性分布模式以及基于参与片段间连接键的原子上的四极展开的特定合成子比对协议来指导。另一方面,在基于合成子的杂化化合物的几何约束对接下对结合位点的适配被用于选择最佳合成子组合。这些策略在识别活性化合物方面表现出与使用完全枚举文库进行的搜索相当的性能,同时计算成本显著降低,为超大型化学空间中的虚拟筛选提供了计算高效的策略。

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