Department of Chemical System Engineering, School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan.
Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, D-53113 Bonn, Germany.
Mol Inform. 2018 Jan;37(1-2). doi: 10.1002/minf.201700103. Epub 2017 Nov 14.
In medicinal chemistry, the molecular scaffolds commonly found in compounds with preferable biological activities are called bioactive scaffolds. They are important because if present in a structure, it is more likely that the compound will be bioactive. Traditionally, medicinal chemists use their knowledge to identify bioactive scaffolds from a given data set after systematic extraction of all candidate scaffolds. However, manually sorting all the scaffolds is not practical as the number of compounds in a data set is often very large. Herein, we propose a method to systematically identify bioactive scaffolds based on a structure generator and a QSAR model. Two proof-of-concept studies showed that known bioactive scaffolds as well as scaffolds containing important substructures were extracted. The proposed method does not depend on scaffold frequencies in a data set, which is different from currently used methods for bioactive scaffold identification.
在药物化学中，具有较好生物活性的化合物中常见的分子骨架称为生物活性骨架。它们很重要，因为如果结构中存在，化合物更有可能具有生物活性。传统上，药物化学家在系统地提取所有候选骨架后，利用他们的知识从给定的数据集识别生物活性骨架。然而，由于数据集中化合物的数量通常非常大，因此手动对所有骨架进行分类是不切实际的。在此，我们提出了一种基于结构生成器和 QSAR 模型系统地识别生物活性骨架的方法。两项概念验证研究表明，已知的生物活性骨架以及包含重要子结构的骨架都被提取出来了。所提出的方法不依赖于数据集中骨架的频率，这与目前用于生物活性骨架识别的方法不同。
