Literature DB >> 22489665

MMP-Cliffs: systematic identification of activity cliffs on the basis of matched molecular pairs.

Xiaoying Hu1, Ye Hu, Martin Vogt, Dagmar Stumpfe, Jürgen Bajorath.   

Abstract

Activity cliffs are generally defined as pairs of structurally similar compounds having large differences in potency. The analysis of activity cliffs is of general interest because structure-activity relationship (SAR) determinants can often be deduced from them. Critical questions for the study of activity cliffs include how similar compounds should be to qualify as cliff partners, how similarity should be assessed, and how large potency differences between participating compounds should be. Thus far, activity cliffs have mostly been defined on the basis of calculated Tanimoto similarity values using structural descriptors, especially 2D fingerprints. As any theoretical assessment of molecular similarity, this approach has its limitations. For example, calculated Tanimoto similarities might often be difficult to reconcile and interpret from a chemical perspective, a point of critique frequently raised in medicinal chemistry. Herein, we have explored activity cliffs by considering well-defined substructure replacements instead of calculated similarity values. For this purpose, the matched molecular pair (MMP) formalism has been applied. MMPs were systematically derived from public domain compounds, and activity cliffs were extracted from them, termed MMP-cliffs. The frequency of cliff formation was determined for compounds active against different targets, MMP-cliffs were analyzed in detail, and re-evaluated on the basis of Tanimoto similarity. In many instances, chemically intuitive activity cliffs were only detected on the basis of MMPs, but not Tanimoto similarity.

Mesh:

Year:  2012        PMID: 22489665     DOI: 10.1021/ci3001138

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  41 in total

1.  Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures.

Authors:  Bijun Zhang; Martin Vogt; Gerald M Maggiora; Jürgen Bajorath
Journal:  J Comput Aided Mol Des       Date:  2015-09-29       Impact factor: 3.686

2.  Comparison of bioactive chemical space networks generated using substructure- and fingerprint-based measures of molecular similarity.

Authors:  Bijun Zhang; Martin Vogt; Gerald M Maggiora; Jürgen Bajorath
Journal:  J Comput Aided Mol Des       Date:  2015-06-07       Impact factor: 3.686

3.  Structural and Activity Profile Relationships Between Drug Scaffolds.

Authors:  Ye Hu; Jürgen Bajorath
Journal:  AAPS J       Date:  2015-02-20       Impact factor: 4.009

4.  Systematic computational identification of promiscuity cliff pathways formed by inhibitors of the human kinome.

Authors:  Filip Miljković; Martin Vogt; Jürgen Bajorath
Journal:  J Comput Aided Mol Des       Date:  2019-03-26       Impact factor: 3.686

5.  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

6.  Structure-Promiscuity Relationship Puzzles-Extensively Assayed Analogs with Large Differences in Target Annotations.

Authors:  Ye Hu; Swarit Jasial; Erik Gilberg; Jürgen Bajorath
Journal:  AAPS J       Date:  2017-03-06       Impact factor: 4.009

7.  The influence of hydrogen bonding on partition coefficients.

Authors:  Nádia Melo Borges; Peter W Kenny; Carlos A Montanari; Igor M Prokopczyk; Jean F R Ribeiro; Josmar R Rocha; Geraldo Rodrigues Sartori
Journal:  J Comput Aided Mol Des       Date:  2017-01-04       Impact factor: 3.686

8.  Exploring ensembles of bioactive or virtual analogs of X-ray ligands for shape similarity searching.

Authors:  Tomoyuki Miyao; Jürgen Bajorath
Journal:  J Comput Aided Mol Des       Date:  2018-07-02       Impact factor: 3.686

9.  Dark chemical matter in public screening assays and derivation of target hypotheses.

Authors:  Swarit Jasial; Jürgen Bajorath
Journal:  Medchemcomm       Date:  2017-10-26       Impact factor: 3.597

10.  Structure-based predictions of activity cliffs.

Authors:  Jarmila Husby; Giovanni Bottegoni; Irina Kufareva; Ruben Abagyan; Andrea Cavalli
Journal:  J Chem Inf Model       Date:  2015-05-11       Impact factor: 4.956

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