title: Maximum common substructure-based Tversky index: an asymmetric hybrid similarity measure.
abstract: Current approaches for the assessment of molecular similarity can generally be divided into descriptor-based and substructure-based methods. The former require the application of similarity metrics that yield continuous similarity values, whereas the readout of the latter is binary (i.e. similar vs. not similar). However, it is also possible to combine descriptor-based and substructure-based methods to exploit advantages of individual methods in context and generate similarity measures for special applications. Herein we present a hybrid measure for asymmetric similarity calculations on the basis of maximum common core structures. This similarity function can be effectively applied to compare small reference compounds with larger test molecules, which is difficult using conventional metrics.
authors: Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, 53113, Bonn, Germany.
authors: Ryo
authors: R
authors: Kunimoto
authors: Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, 53113, Bonn, Germany.
authors: Martin
authors: M
authors: Vogt
authors: Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, 53113, Bonn, Germany. bajorath@bit.uni-bonn.de.
authors: Jürgen
authors: J
authors: Bajorath
mesh_terms: D000465:Algorithms; D008958:Models, Molecular; D013329:Structure-Activity Relationship
keywords: Hybrid measures; Maximum common substructure; Molecular similarity; Similarity metrics; Substructure methods; Tversky similarity