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LSA: a local-weighted structural alignment tool for pharmaceutical virtual screening

机译：LSA：用于制药虚拟筛选的局部加权结构对齐工具

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Similar structures having similar activities is a dogma for identifying new functional molecules. However, it is not rare that a minor structural change can cause a significant activity change. Methods to measure the molecular similarity can be classified into two categories of overall three-dimensional shape based methods and local substructure based methods. The former states the relation between overall similarity and activity, and is represented by conventional similarity algorithms. The latter states the relation between local substructure and activity, and is represented by conventional substructure match algorithms. Practically, the similarity of two molecules with similar activity depends on the contributions from both overall similarity and local substructure match. We report a new tool termed as a local-weighted structural alignment (LSA) tool for pharmaceutical virtual screening, which computes the similarity of two molecular structures by considering the contributions of both overall similarity and local substructure match. LSA consists of three steps: (1) mapping a common substructure between two molecular topological structures; (2) superimposing two three-dimensional molecular structures with substructure focus; (3) computing the similarity score based on superimposing. LSA has been validated with 102 testing compound libraries from DUD-E collection with the average AUC (the area under a receiver-operating characteristic curve) value of 0.82 and an average EF ~(1%) (the enrichment factor at top 1%) of 27.0, which had consistently better performance than conventional approaches. LSA is implemented in C++ and run on Linux and Windows systems.
机译：具有类似活动的类似结构是用于识别新功能分子的教条。但是，少量结构变化可能导致显着的活动变化并不罕见。测量分子相似性的方法可以分为两类总基于三维形状的方法和基于局部子结构的方法。前者说明了总体相似性和活动之间的关系，并由常规相似性算法表示。后者状态指出了本地子结构和活动之间的关系，并且由传统的子结构匹配算法表示。实际上，两个具有相似活动的分子的相似性取决于总体相似性和局部子结构匹配的贡献。我们向药物虚拟筛选的本地加权结构对准（LSA）工具报告了一个称为用于药物虚拟筛选的新工具，其通过考虑整体相似性和本地子结构匹配的贡献来计算两个分子结构的相似性。 LSA由三个步骤组成：（1）在两个分子拓扑结构之间映射常见的子结构; （2）用子结构焦点叠加两个三维分子结构; （3）计算基于叠加的相似度分数。 LSA已被DUD-E收集的102个复合库验证，平均AUC（接收器操作特性曲线的区域）值为0.82和平均EF〜（1％）（富集因子为前1％的富集因子） 27.0，这一直比传统方法始终更好。 LSA在C ++中实现，并在Linux和Windows系统上运行。

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