Literature DB >> 31272836

Introducing a new category of activity cliffs with chemical modifications at multiple sites and rationalizing contributions of individual substitutions.

Dagmar Stumpfe1, Huabin Hu1, Jürgen Bajorath2.   

Abstract

Activity cliffs (ACs) are formed by structurally similar active compounds with large potency differences. In medicinal chemistry, ACs are of high interest because they reveal structure-activity relationship (SAR) information and SAR determinants. Herein, we introduce a new type of ACs that consist of analog pairs with different substitutions at multiple sites (multi-site ACs; msACs). A systematic search for msACs across different classes of bioactive compounds identified more than 4000 of such ACs, most of which had substitutions at two sites (dual-site ACs; dsACs). A hierarchical analog data structure was designed to analyze contributions of individual substitutions to AC formation. Single substitutions were frequently found to determine potency differences captured by dsACs. Hence, in such cases, there was redundancy of AC information. In instances where both substitutions made significant contributions to dsACs, additive, synergistic, and compensatory effects were observed. Taken together, the results of our analysis revealed the prevalence of single-site ACs (ssACs) in analog series, followed by dsACs, which reveal different ways in which paired substitutions contribute to the formation of ACs and modulate SARs.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Activity cliffs; Analog pairs; Analog series; Differential substitution effects; Multi-site activity cliffs; Multiple substitutions; Potency differences; Structure-activity relationships

Year:  2019        PMID: 31272836     DOI: 10.1016/j.bmc.2019.06.045

Source DB:  PubMed          Journal:  Bioorg Med Chem        ISSN: 0968-0896            Impact factor:   3.641


  4 in total

1.  Introducing a new category of activity cliffs combining different compound similarity criteria.

Authors:  Huabin Hu; Jürgen Bajorath
Journal:  RSC Med Chem       Date:  2020-01-07

2.  Advances in exploring activity cliffs.

Authors:  Dagmar Stumpfe; Huabin Hu; Jürgen Bajorath
Journal:  J Comput Aided Mol Des       Date:  2020-05-05       Impact factor: 3.686

3.  Prediction Model with High-Performance Constitutive Androstane Receptor (CAR) Using DeepSnap-Deep Learning Approach from the Tox21 10K Compound Library.

Authors:  Yasunari Matsuzaka; Yoshihiro Uesawa
Journal:  Int J Mol Sci       Date:  2019-09-30       Impact factor: 5.923

4.  Increasing the public activity cliff knowledge base with new categories of activity cliffs.

Authors:  Huabin Hu; Jürgen Bajorath
Journal:  Future Sci OA       Date:  2020-04-15
  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.