Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, 53113, Bonn, Germany.
J Comput Aided Mol Des. 2013 Aug;27(8):665-74. doi: 10.1007/s10822-013-9671-5. Epub 2013 Aug 24.
We have aimed to systematically extract analog series with related core structures from multi-target activity space to explore target promiscuity of closely related analogous. Therefore, a previously introduced SAR matrix structure was adapted and further extended for large-scale data mining. These matrices organize analog series with related yet distinct core structures in a consistent manner. High-confidence compound activity data yielded more than 2,300 non-redundant matrices capturing 5,821 analog series that included 4,288 series with multi-target and 735 series with multi-family activities. Many matrices captured more than three analog series with activity against more than five targets. The matrices revealed a variety of promiscuity patterns. Compound series matrices also contain virtual compounds, which provide suggestions for compound design focusing on desired activity profiles.
我们旨在从多靶点活性空间中系统地提取具有相关核心结构的类似物系列，以探索密切相关类似物的靶标混杂性。因此，我们对之前介绍的 SAR 矩阵结构进行了改编和进一步扩展，以进行大规模数据挖掘。这些矩阵以一致的方式组织具有相关但不同核心结构的类似物系列。高可信度的化合物活性数据产生了 2300 多个非冗余矩阵，捕获了 5821 个类似物系列，其中包括 4288 个具有多靶点活性的系列和 735 个具有多家族活性的系列。许多矩阵捕获了 3 个以上的类似物系列，这些类似物系列对 5 个以上的靶点具有活性。这些矩阵揭示了各种混杂模式。化合物系列矩阵还包含虚拟化合物，这些化合物为化合物设计提供了建议，重点是所需的活性谱。
