Zhu Fangqiang, Agrafiotis Dimitris K
Johnson & Johnson Pharmaceutical Research and Development, L.L.C. 665 Stockton Drive, Exton, Pennsylvania 19341, USA.
J Chem Inf Model. 2007 Jul-Aug;47(4):1619-25. doi: 10.1021/ci7000583. Epub 2007 Jun 5.
An improved method for exhaustively identifying common pharmacophores from a given list of 3D conformers is proposed. The method partitions feature lists into multidimensional boxes according to the distances between the pharmacophore centers. Unlike some existing techniques, each feature list is mapped into multiple boxes to ensure that good matches will never be missed due to the partitioning. To circumvent the computational complexity of the problem, a recursive distance partitioning (RDP) algorithm is introduced, in which the partitioning and the elimination of unqualified feature lists are carried out at multiple levels. The method is demonstrated to be both accurate and efficient.
提出了一种从给定的3D构象异构体列表中全面识别常见药效团的改进方法。该方法根据药效团中心之间的距离将特征列表划分为多维盒子。与一些现有技术不同，每个特征列表被映射到多个盒子中，以确保不会因划分而错过良好匹配。为了规避该问题的计算复杂性，引入了一种递归距离划分（RDP）算法，其中划分和不合格特征列表的消除在多个级别上进行。结果表明该方法既准确又高效。
