Riemannian geometry and molecular similarity I: spectrum of the Laplacian
Riemannian geometry and molecular similarity I: spectrum of the Laplacian
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黎曼几何和分子相似性 I：拉普拉斯谱
DOI：
10.1098/rspa.2023.0343
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发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Hall S
中科院分区：
文献类型：
--
作者：
Hall S
基于配体的虚拟筛选旨在降低小分子药物发现活动的成本和持续时间。形状相似性可用于筛选大型数据库，目标是通过与具有已知有利性质的分子进行比较来预测潜在的新命中。本文介绍了RGMolSA的理论基础，一个新的无网格和无网格的表面为基础的分子形状描述符来自黎曼几何的数学理论。把分子看作一系列相交的球体，可以用黎曼度量来描述其表面几何，黎曼度量是通过考虑拉普拉斯算子的谱而得到的。这给出了一个简单的向量描述符，由加权表面积和八个非零特征值构成，这些特征值捕获了表面形状。我们通过考虑一系列已知具有类似形状的PDE5抑制剂作为初始测试案例来证明我们方法的潜力。RGMolSA显示承诺相比，现有的形状描述符，并在其处理不同的分子构象的能力。
Ligand-based virtual screening aims to reduce the cost and duration of small molecule drug discovery campaigns. Shape similarity can be used to screen large databases, with the goal of predicting potential new hits by comparing with molecules with known favourable properties. This paper presents the theory underpinning RGMolSA, a new alignment-free and mesh-free surface-based molecular shape descriptor derived from the mathematical theory of Riemannian geometry. The treatment of a molecule as a series of intersecting spheres allows the description of its surface geometry using theRiemannian metric, obtained by considering the spectrum of the Laplacian. This gives a simple vector descriptor constructed of the weighted surface area and eight non-zero eigenvalues, which capture the surface shape. We demonstrate the potential of our method by considering a series of PDE5 inhibitors that are known to have similar shape as an initial test case. RGMolSA displays promise when compared with existing shape descriptors and in its capability to handle different molecular conformers.
UFSRAT：超快速原子类型形状识别 - FKBP12 和 11βHSD1 新型生物活性小分子支架的发现
影响因子：
3.7
作者：
S. Shave;E. Blackburn;Jillian Adie;D. Houston;M. Auer;S. Webster;P. Taylor;M. Walkinshaw
通讯作者：
M. Walkinshaw