Context-dependent similarity analysis of analogue series for structure-activity relationship transfer based on a concept from natural language processing

作     者：Yoshimori, Atsushi Bajorath, Jurgen 

作者机构：Inst Theoret Med Inc 26-1 Muraoka Higashi 2 chome Fujisawa Kanagawa 2510012 Japan Univ Bonn Dept Life Sci Informat & Data Sci LIMES Program Unit Chem Biol & Med Chem B IT Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany Univ Bonn Lamarr Inst Machine Learning & Artif Intelligence Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany 

出 版 物：《JOURNAL OF CHEMINFORMATICS》 (J. Cheminformatics)

年 卷 期：2025年第17卷第1期

页      面：1-14页

核心收录：

学科分类：081704[工学-应用化学] 08[工学] 0817[工学-化学工程与技术] 0703[理学-化学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Projekt DEAL 

主　　题：Active analogue series Potency progression Series alignment SAR transfer Fragment pair relationships Context-dependent similarity 

摘      要：Analogue series (AS) are generated during compound optimization in medicinal chemistry and are the major source of structure-activity relationship (SAR) information. Pairs of active AS consisting of compounds with corresponding substituents and comparable potency progression represent SAR transfer events for the same target or across different targets. We report a new computational approach to systematically search for SAR transfer series that combines an AS alignment algorithm with context-depending similarity assessment based on vector embeddings adapted from natural language processing. The methodology comprehensively accounts for substituent similarity, identifies non-classical bioisosteres, captures substituent-property relationships, and generates accurate AS alignments. Context-dependent similarity assessment is conceptually novel in computational medicinal chemistry and should also be of interest for other *** contributionA method is reported to systematically search for and align analogue series with SAR transfer potential. Central to the approach is the assessment of context-dependent similarity for substituents, a new concept in cheminformatics, which is based upon vector embeddings and word pair relationships adapted from natural language processing.