Publication

Systems chemistry: using thermodynamically controlled networks to assess molecular similarity

Saggiomo, V., Hristova, Y. R., Ludlow, R. F. & Otto, S., 12-Feb-2013, In : Journal of Systems Chemistry. 4, 2, 6 p.

Research output: Contribution to journalArticleAcademicpeer-review

APA

Saggiomo, V., Hristova, Y. R., Ludlow, R. F., & Otto, S. (2013). Systems chemistry: using thermodynamically controlled networks to assess molecular similarity. Journal of Systems Chemistry, 4(2). https://doi.org/10.1186/1759-2208-4-2

Author

Saggiomo, Vittorio ; Hristova, Yana R. ; Ludlow, R. Frederick ; Otto, Sijbren. / Systems chemistry : using thermodynamically controlled networks to assess molecular similarity. In: Journal of Systems Chemistry. 2013 ; Vol. 4, No. 2.

Harvard

Saggiomo, V, Hristova, YR, Ludlow, RF & Otto, S 2013, 'Systems chemistry: using thermodynamically controlled networks to assess molecular similarity', Journal of Systems Chemistry, vol. 4, no. 2. https://doi.org/10.1186/1759-2208-4-2

Standard

Systems chemistry : using thermodynamically controlled networks to assess molecular similarity. / Saggiomo, Vittorio; Hristova, Yana R.; Ludlow, R. Frederick; Otto, Sijbren.

In: Journal of Systems Chemistry, Vol. 4, No. 2, 12.02.2013.

Research output: Contribution to journalArticleAcademicpeer-review

Vancouver

Saggiomo V, Hristova YR, Ludlow RF, Otto S. Systems chemistry: using thermodynamically controlled networks to assess molecular similarity. Journal of Systems Chemistry. 2013 Feb 12;4(2). https://doi.org/10.1186/1759-2208-4-2


BibTeX

@article{67bc4562e8ae494fab110bff3a8286cd,
title = "Systems chemistry: using thermodynamically controlled networks to assess molecular similarity",
abstract = "Background: The assessment of mol. similarity is a key step in the drug discovery process that has thus far relied almost exclusively on computational approaches. We now report an exptl. method for similarity assessment based on dynamic combinatorial chem. Results: In order to assess mol. similarity directly in soln., a dynamic mol. network was used in a two-step process. First, a clustering anal. was employed to det. the network's innate discriminatory ability. A classification algorithm was then trained to enable the classification of unknowns. The dynamic mol. network used in this work was able to identify thin amines and ammonium ions in a set of 25 different, closely related mols. After training, it was also able to classify unknown mols. based on the presence or absence of an ethylamine group. Conclusions: This is the first step in the development of mol. networks capable of predicting bioactivity based on an assessment of mol. similarity. [on SciFinder(R)]",
keywords = "Clustering analysis, Data mining, Molecular networks, Systems chemistry, Dynamic combinatorial chemistry",
author = "Vittorio Saggiomo and Hristova, {Yana R.} and Ludlow, {R. Frederick} and Sijbren Otto",
note = "Relation: https://www.rug.nl/research/stratingh/ date_submitted:2014 Rights: University of Groningen, Stratingh Institute for Chemistry",
year = "2013",
month = "2",
day = "12",
doi = "10.1186/1759-2208-4-2",
language = "English",
volume = "4",
journal = "Journal of Systems Chemistry",
issn = "1759-2208",
number = "2",

}

RIS

TY - JOUR

T1 - Systems chemistry

T2 - using thermodynamically controlled networks to assess molecular similarity

AU - Saggiomo, Vittorio

AU - Hristova, Yana R.

AU - Ludlow, R. Frederick

AU - Otto, Sijbren

N1 - Relation: https://www.rug.nl/research/stratingh/ date_submitted:2014 Rights: University of Groningen, Stratingh Institute for Chemistry

PY - 2013/2/12

Y1 - 2013/2/12

N2 - Background: The assessment of mol. similarity is a key step in the drug discovery process that has thus far relied almost exclusively on computational approaches. We now report an exptl. method for similarity assessment based on dynamic combinatorial chem. Results: In order to assess mol. similarity directly in soln., a dynamic mol. network was used in a two-step process. First, a clustering anal. was employed to det. the network's innate discriminatory ability. A classification algorithm was then trained to enable the classification of unknowns. The dynamic mol. network used in this work was able to identify thin amines and ammonium ions in a set of 25 different, closely related mols. After training, it was also able to classify unknown mols. based on the presence or absence of an ethylamine group. Conclusions: This is the first step in the development of mol. networks capable of predicting bioactivity based on an assessment of mol. similarity. [on SciFinder(R)]

AB - Background: The assessment of mol. similarity is a key step in the drug discovery process that has thus far relied almost exclusively on computational approaches. We now report an exptl. method for similarity assessment based on dynamic combinatorial chem. Results: In order to assess mol. similarity directly in soln., a dynamic mol. network was used in a two-step process. First, a clustering anal. was employed to det. the network's innate discriminatory ability. A classification algorithm was then trained to enable the classification of unknowns. The dynamic mol. network used in this work was able to identify thin amines and ammonium ions in a set of 25 different, closely related mols. After training, it was also able to classify unknown mols. based on the presence or absence of an ethylamine group. Conclusions: This is the first step in the development of mol. networks capable of predicting bioactivity based on an assessment of mol. similarity. [on SciFinder(R)]

KW - Clustering analysis

KW - Data mining

KW - Molecular networks

KW - Systems chemistry

KW - Dynamic combinatorial chemistry

U2 - 10.1186/1759-2208-4-2

DO - 10.1186/1759-2208-4-2

M3 - Article

VL - 4

JO - Journal of Systems Chemistry

JF - Journal of Systems Chemistry

SN - 1759-2208

IS - 2

ER -

ID: 2340473