Ebalunode Jerry Osagie, Zheng Weifan
Department of Pharmaceutical Sciences, BRITE Institute, North Carolina Central University, Durham, North Carolina 27707, USA.
J Chem Inf Model. 2009 Jun;49(6):1313-20. doi: 10.1021/ci900015b.
3D molecular shape similarity search has recently become an attractive method for virtual screening and scaffold hopping in drug discovery and chemical genomics research. Among these 3D similarity methods is ROCS (Rapid Overlay of Chemical Structures), a popular tool because of its efficiency and effectiveness. However, searching a large multiconformer molecular database remains a very challenging task because of the nature of such calculations. To simplify shape similarity calculations and potentially increase the efficiency for large scale virtual screening, we have explored an alternative shape similarity approach that does not depend on multiconformers of molecules. The hypothesis underlying this approach is that similar chemical structures tend to have similar 2D chemical depictions and that shape comparison techniques can be utilized to effectively compare the shapes between chemical depictions. We use a 2D depiction program to generate 2-D chemical drawings for both the query molecule and database molecules. We have built a 2D shape comparison program based on the OESHAPE Toolkit (OE Scientific, NM) that compares the molecular depictions and quantifies the shape similarity between the molecules. We demonstrate that this unconventional 2D shape similarity method performs fairly well in virtual screening experiments compared to the 3D Shape method ROCS, with an added advantage of its computational efficiency.
3D分子形状相似性搜索最近已成为药物发现和化学基因组学研究中虚拟筛选和骨架跃迁的一种有吸引力的方法。在这些3D相似性方法中，ROCS（化学结构快速叠加）是一种流行的工具，因其效率和有效性而受到青睐。然而，由于此类计算的性质，搜索大型多构象分子数据库仍然是一项极具挑战性的任务。为了简化形状相似性计算并可能提高大规模虚拟筛选的效率，我们探索了一种不依赖于分子多构象的替代形状相似性方法。这种方法的基本假设是，相似的化学结构往往具有相似的二维化学描绘，并且形状比较技术可用于有效比较化学描绘之间的形状。我们使用二维描绘程序为查询分子和数据库分子生成二维化学绘图。我们基于OESHAPE Toolkit（OE Scientific，新墨西哥州）构建了一个二维形状比较程序，该程序比较分子描绘并量化分子之间的形状相似性。我们证明，与3D形状方法ROCS相比，这种非常规的二维形状相似性方法在虚拟筛选实验中表现相当出色，并且具有计算效率方面的额外优势。
