Drug targets prediction using chemical similarity. / Galeano Galeano, Diego; Paccanaro, Alberto.

XLII Conferencia Latinoamericana de Informatica (CLEI). IEEE Xplore, 2017. p. 1-7 (2016 XLII Latin American Computing Conference (CLEI)).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Published

Standard

Drug targets prediction using chemical similarity. / Galeano Galeano, Diego; Paccanaro, Alberto.

XLII Conferencia Latinoamericana de Informatica (CLEI). IEEE Xplore, 2017. p. 1-7 (2016 XLII Latin American Computing Conference (CLEI)).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Galeano Galeano, D & Paccanaro, A 2017, Drug targets prediction using chemical similarity. in XLII Conferencia Latinoamericana de Informatica (CLEI). 2016 XLII Latin American Computing Conference (CLEI), IEEE Xplore, pp. 1-7. https://doi.org/10.1109/CLEI.2016.7833353

APA

Galeano Galeano, D., & Paccanaro, A. (2017). Drug targets prediction using chemical similarity. In XLII Conferencia Latinoamericana de Informatica (CLEI) (pp. 1-7). (2016 XLII Latin American Computing Conference (CLEI)). IEEE Xplore. https://doi.org/10.1109/CLEI.2016.7833353

Vancouver

Galeano Galeano D, Paccanaro A. Drug targets prediction using chemical similarity. In XLII Conferencia Latinoamericana de Informatica (CLEI). IEEE Xplore. 2017. p. 1-7. (2016 XLII Latin American Computing Conference (CLEI)). https://doi.org/10.1109/CLEI.2016.7833353

Author

Galeano Galeano, Diego ; Paccanaro, Alberto. / Drug targets prediction using chemical similarity. XLII Conferencia Latinoamericana de Informatica (CLEI). IEEE Xplore, 2017. pp. 1-7 (2016 XLII Latin American Computing Conference (CLEI)).

BibTeX

@inproceedings{06c49d52d1db47218fe898eea7327794,
title = "Drug targets prediction using chemical similarity",
abstract = "The growing productivity gap between investment in drug research and development (R&D) and the number of new medicines approved by the US Food and Drug Administration (FDA) in the past decade is concerning. This productivity problem raises the need for innovative approaches for drug-target prediction and a deeper understanding of the interplay between drugs and their target proteins. Chemogenomics is the interdisciplinary field which aims to predict gene/protein/ligand relationships. The predictions are based on the assumption that chemically similar compounds should share common targets. Here, we exploit our understanding of the network-based representation of the protein-protein interaction (PPI network) to introduce a distance between drug-targets and could verify whether it correlates with their chemical similarity. We build a fully connected graph composed of US Food and Drug Administration (FDA) - approved drugs using the Tanimoto 2D similarity based on fingerprints from the SMILES representation of the chemical structure. Our analysis of 1165 FDA-approved drugs indicates that the chemical similarity of drugs predicts closeness of their targets in the human interactome.",
keywords = "Drugs, Proteins, Chemicals, Diseases, Databases, Compounds",
author = "{Galeano Galeano}, Diego and Alberto Paccanaro",
year = "2017",
month = jan,
day = "26",
doi = "10.1109/CLEI.2016.7833353",
language = "English",
isbn = "978-1-5090-1634-1",
series = "2016 XLII Latin American Computing Conference (CLEI)",
publisher = "IEEE Xplore",
pages = "1--7",
booktitle = "XLII Conferencia Latinoamericana de Informatica (CLEI)",

}

RIS

TY - GEN

T1 - Drug targets prediction using chemical similarity

AU - Galeano Galeano, Diego

AU - Paccanaro, Alberto

PY - 2017/1/26

Y1 - 2017/1/26

N2 - The growing productivity gap between investment in drug research and development (R&D) and the number of new medicines approved by the US Food and Drug Administration (FDA) in the past decade is concerning. This productivity problem raises the need for innovative approaches for drug-target prediction and a deeper understanding of the interplay between drugs and their target proteins. Chemogenomics is the interdisciplinary field which aims to predict gene/protein/ligand relationships. The predictions are based on the assumption that chemically similar compounds should share common targets. Here, we exploit our understanding of the network-based representation of the protein-protein interaction (PPI network) to introduce a distance between drug-targets and could verify whether it correlates with their chemical similarity. We build a fully connected graph composed of US Food and Drug Administration (FDA) - approved drugs using the Tanimoto 2D similarity based on fingerprints from the SMILES representation of the chemical structure. Our analysis of 1165 FDA-approved drugs indicates that the chemical similarity of drugs predicts closeness of their targets in the human interactome.

AB - The growing productivity gap between investment in drug research and development (R&D) and the number of new medicines approved by the US Food and Drug Administration (FDA) in the past decade is concerning. This productivity problem raises the need for innovative approaches for drug-target prediction and a deeper understanding of the interplay between drugs and their target proteins. Chemogenomics is the interdisciplinary field which aims to predict gene/protein/ligand relationships. The predictions are based on the assumption that chemically similar compounds should share common targets. Here, we exploit our understanding of the network-based representation of the protein-protein interaction (PPI network) to introduce a distance between drug-targets and could verify whether it correlates with their chemical similarity. We build a fully connected graph composed of US Food and Drug Administration (FDA) - approved drugs using the Tanimoto 2D similarity based on fingerprints from the SMILES representation of the chemical structure. Our analysis of 1165 FDA-approved drugs indicates that the chemical similarity of drugs predicts closeness of their targets in the human interactome.

KW - Drugs, Proteins, Chemicals, Diseases, Databases, Compounds

U2 - 10.1109/CLEI.2016.7833353

DO - 10.1109/CLEI.2016.7833353

M3 - Conference contribution

SN - 978-1-5090-1634-1

T3 - 2016 XLII Latin American Computing Conference (CLEI)

SP - 1

EP - 7

BT - XLII Conferencia Latinoamericana de Informatica (CLEI)

PB - IEEE Xplore

ER -