Podolyan Yevgeniy, Karypis George
University of Minnesota, Department of Computer Science and Computer Engineering, Minneapolis, Minnesota 55455, USA.
J Chem Inf Model. 2009 Jan;49(1):13-21. doi: 10.1021/ci8002478.
The knowledge of a pharmacophore, or the 3D arrangement of features in the biologically active molecule that is responsible for its pharmacological activity, can help in the search and design of a new or better drug acting upon the same or related target. In this paper, we describe two new algorithms based on the frequent clique detection in the molecular graphs. The first algorithm mines all frequent cliques that are present in at least one of the conformers of each (or a portion of all) molecules. The second algorithm exploits the similarities among the different conformers of the same molecule and achieves an order of magnitude performance speedup compared to the first algorithm. Both algorithms are guaranteed to find all common pharmacophores in the data set, which is confirmed by the validation on the set of molecules for which pharmacophores have been determined experimentally. In addition, these algorithms are able to scale to data sets with arbitrarily large number of conformers per molecule and identify multiple ligand binding modes or multiple binding sites of the target.
药效团知识，即生物活性分子中负责其药理活性的特征的三维排列，有助于寻找和设计作用于相同或相关靶点的新的或更好的药物。在本文中，我们描述了两种基于分子图中频繁团检测的新算法。第一种算法挖掘每个（或所有分子的一部分）分子的至少一个构象中存在的所有频繁团。第二种算法利用同一分子不同构象之间的相似性，与第一种算法相比，实现了一个数量级的性能加速。两种算法都能保证找到数据集中所有常见的药效团，这在已通过实验确定药效团的分子集上的验证中得到了证实。此外，这些算法能够扩展到每个分子具有任意大量构象的数据集，并识别靶点的多种配体结合模式或多个结合位点。
