Laufkötter Oliver, Miyao Tomoyuki, Bajorath Jürgen
Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, D-53115 Bonn, Germany.
Data Science Center and Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan.
ACS Omega. 2019 Sep 5;4(12):15304-15311. doi: 10.1021/acsomega.9b02470. eCollection 2019 Sep 17.
Similarity searching (SS) is a core approach in computational compound screening and has a long tradition in pharmaceutical research. Over the years, different approaches have been introduced to increase the information content of search calculations and optimize the ability to detect compounds having similar activity. We present a large-scale comparison of distinct search strategies on more than 600 qualifying compound activity classes. Challenging test cases for SS were identified and used to evaluate different ways to further improve search performance, which provided a differentiated view of alternative search strategies and their relative performance. It was found that search results could not only be improved by increasing compound input information but also by focusing similarity calculations on database compounds. In the presence of multiple active reference compounds, asymmetric SS with high weights on chemical features of target compounds emerged as an overall preferred approach across many different activity classes. These findings have implications for practical virtual screening applications.
相似性搜索（SS）是计算化合物筛选中的一种核心方法，在药物研究领域有着悠久的传统。多年来，人们引入了不同的方法来增加搜索计算的信息含量，并优化检测具有相似活性化合物的能力。我们对600多个合格的化合物活性类别进行了不同搜索策略的大规模比较。确定了SS的具有挑战性的测试用例，并用于评估进一步提高搜索性能的不同方法，这为替代搜索策略及其相对性能提供了差异化的视角。研究发现，不仅可以通过增加化合物输入信息来改善搜索结果，还可以通过将相似性计算集中在数据库化合物上来实现。在存在多个活性参考化合物的情况下，对目标化合物的化学特征赋予高权重的不对称SS在许多不同的活性类别中成为总体上首选的方法。这些发现对实际的虚拟筛选应用具有启示意义。
