Literature DB >> 21609014

Consensus models of activity landscapes with multiple chemical, conformer, and property representations.

Austin B Yongye1, Kendall Byler, Radleigh Santos, Karina Martínez-Mayorga, Gerald M Maggiora, José L Medina-Franco.   

Abstract

We report consensus Structure-Activity Similarity (SAS) maps that address the dependence of activity landscapes on molecular representation. As a case study, we characterized the activity landscape of 54 compounds with activities against human cathepsin B (hCatB), human cathepsin L (hCatL), and Trypanosoma brucei cathepsin B (TbCatB). Starting from an initial set of 28 descriptors we selected ten representations that capture different aspects of the chemical structures. These included four 2D (MACCS keys, GpiDAPH3, pairwise, and radial fingerprints) and six 3D (4p and piDAPH4 fingerprints with each including three conformers) representations. Multiple conformers are used for the first time in consensus activity landscape modeling. The results emphasize the feasibility of identifying consensus data points that are consistently formed in different reference spaces generated with several fingerprint models, including multiple 3D conformers. Consensus data points are not meant to eliminate data, disregarding, for example, "true" activity cliffs that are not identified by some molecular representations. Instead, consensus models are designed to prioritize the SAR analysis of activity cliffs and other consistent regions in the activity landscape that are captured by several molecular representations. Systematic description of the SARs of two targets give rise to the identification of pairs of compounds located in the same region of the activity landscape of hCatL and TbCatB suggesting similar mechanisms of action for the pairs involved. We also explored the relationship between property similarity and activity similarity and found that property similarities are suitable to characterize SARs. We also introduce the concept of structure-property-activity (SPA) similarity in SAR studies.

Entities:  

Mesh:

Substances:

Year:  2011        PMID: 21609014     DOI: 10.1021/ci200081k

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


  12 in total

1.  Activity cliffs and activity cliff generators based on chemotype-related activity landscapes.

Authors:  Jaime Pérez-Villanueva; Oscar Méndez-Lucio; Olivia Soria-Arteche; José L Medina-Franco
Journal:  Mol Divers       Date:  2015-07-07       Impact factor: 2.943

2.  Analysis of structure-Caco-2 permeability relationships using a property landscape approach.

Authors:  Yareli Rojas-Aguirre; José L Medina-Franco
Journal:  Mol Divers       Date:  2014-04-08       Impact factor: 2.943

3.  Docking of a novel DNA methyltransferase inhibitor identified from high-throughput screening: insights to unveil inhibitors in chemical databases.

Authors:  José L Medina-Franco; Jakyung Yoo
Journal:  Mol Divers       Date:  2013-02-28       Impact factor: 2.943

4.  Rapid scanning structure-activity relationships in combinatorial data sets: identification of activity switches.

Authors:  José L Medina-Franco; Bruce S Edwards; Clemencia Pinilla; Jon R Appel; Marc A Giulianotti; Radleigh G Santos; Austin B Yongye; Larry A Sklar; Richard A Houghten
Journal:  J Chem Inf Model       Date:  2013-06-07       Impact factor: 4.956

Review 5.  On exploring structure-activity relationships.

Authors:  Rajarshi Guha
Journal:  Methods Mol Biol       Date:  2013

6.  Exploring Structure-Activity Data Using the Landscape Paradigm.

Authors:  Rajarshi Guha
Journal:  Wiley Interdiscip Rev Comput Mol Sci       Date:  2012-11

7.  Conditional probabilistic analysis for prediction of the activity landscape and relative compound activities.

Authors:  Radleigh G Santos; Marc A Giulianotti; Richard A Houghten; José L Medina-Franco
Journal:  J Chem Inf Model       Date:  2013-09-17       Impact factor: 4.956

8.  Chemoinformatic analysis of GRAS (Generally Recognized as Safe) flavor chemicals and natural products.

Authors:  José L Medina-Franco; Karina Martínez-Mayorga; Terry L Peppard; Alberto Del Rio
Journal:  PLoS One       Date:  2012-11-30       Impact factor: 3.240

9.  Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees.

Authors:  Li Li; Ching Chiek Koh; Daniel Reker; J B Brown; Haishuai Wang; Nicholas Keone Lee; Hien-Haw Liow; Hao Dai; Huai-Meng Fan; Luonan Chen; Dong-Qing Wei
Journal:  Sci Rep       Date:  2019-05-22       Impact factor: 4.379

10.  On the validity versus utility of activity landscapes: are all activity cliffs statistically significant?

Authors:  Rajarshi Guha; José L Medina-Franco
Journal:  J Cheminform       Date:  2014-04-02       Impact factor: 5.514

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.