Fragment-Based Prediction of Cytochromes P450 2D6 and 1A2 Inhibition by Recursive Partitioning

Research output: Contribution to journalArticle

Abstract

The evaluation of the ADME (absorption, distribution, metabolism, and excretion) properties of drug candidates is an important stage in drug discovery. To speed up the numerous tests carried out on large databases of compounds, the help of robust and accurate in silico filters is increasingly required. We propose here a method to build predictive and interpretable models for the prediction of cytochrome P450 (CYP) 1A2 and 2D6 inhibition using recursive partitioning (RP), a well-known technique for the construction of decision trees. The originality of the work is the use of several descriptions of the molecules in terms of fragments, i.e. the MACCS keys and five in-house fingerprints based on the electron density properties of fragments, employed to draw easily understandable structure-activity models. The classifiers reached performances of 87.5% and 76.5% of prediction on a validation set for CYP1A2 and CYP2D6, respectively. The analysis of the first nodes of the RP trees permits us to highlight some relations between the structural fragments and the inhibition of CYPs.
Original languageEnglish
Pages (from-to)185-205
Number of pages21
JournalSAR QSAR Environ. Res.
Volume20
DOIs
Publication statusPublished - 1 Jan 2009

Fingerprint

Cytochrome P-450 CYP1A2
Cytochrome P-450 CYP2D6
Decision Trees
Dermatoglyphics
Drug Discovery
Decision trees
Metabolism
Computer Simulation
Carrier concentration
Classifiers
Databases
Electrons
Molecules
Pharmaceutical Preparations
Cytochrome P-450 Enzyme System

Cite this

@article{28d928b18c5c4a63a429dc3afd61750d,
title = "Fragment-Based Prediction of Cytochromes P450 2D6 and 1A2 Inhibition by Recursive Partitioning",
abstract = "The evaluation of the ADME (absorption, distribution, metabolism, and excretion) properties of drug candidates is an important stage in drug discovery. To speed up the numerous tests carried out on large databases of compounds, the help of robust and accurate in silico filters is increasingly required. We propose here a method to build predictive and interpretable models for the prediction of cytochrome P450 (CYP) 1A2 and 2D6 inhibition using recursive partitioning (RP), a well-known technique for the construction of decision trees. The originality of the work is the use of several descriptions of the molecules in terms of fragments, i.e. the MACCS keys and five in-house fingerprints based on the electron density properties of fragments, employed to draw easily understandable structure-activity models. The classifiers reached performances of 87.5{\%} and 76.5{\%} of prediction on a validation set for CYP1A2 and CYP2D6, respectively. The analysis of the first nodes of the RP trees permits us to highlight some relations between the structural fragments and the inhibition of CYPs.",
author = "J. Burton and E. Danloy and D.P. Vercauteren",
note = "Copyright 2010 Elsevier B.V., All rights reserved.",
year = "2009",
month = "1",
day = "1",
doi = "10.1080/10629360902726650",
language = "English",
volume = "20",
pages = "185--205",
journal = "SAR and QSAR in environmental research",
issn = "1062-936X",
publisher = "Taylor & Francis",

}

TY - JOUR

T1 - Fragment-Based Prediction of Cytochromes P450 2D6 and 1A2 Inhibition by Recursive Partitioning

AU - Burton, J.

AU - Danloy, E.

AU - Vercauteren, D.P.

N1 - Copyright 2010 Elsevier B.V., All rights reserved.

PY - 2009/1/1

Y1 - 2009/1/1

N2 - The evaluation of the ADME (absorption, distribution, metabolism, and excretion) properties of drug candidates is an important stage in drug discovery. To speed up the numerous tests carried out on large databases of compounds, the help of robust and accurate in silico filters is increasingly required. We propose here a method to build predictive and interpretable models for the prediction of cytochrome P450 (CYP) 1A2 and 2D6 inhibition using recursive partitioning (RP), a well-known technique for the construction of decision trees. The originality of the work is the use of several descriptions of the molecules in terms of fragments, i.e. the MACCS keys and five in-house fingerprints based on the electron density properties of fragments, employed to draw easily understandable structure-activity models. The classifiers reached performances of 87.5% and 76.5% of prediction on a validation set for CYP1A2 and CYP2D6, respectively. The analysis of the first nodes of the RP trees permits us to highlight some relations between the structural fragments and the inhibition of CYPs.

AB - The evaluation of the ADME (absorption, distribution, metabolism, and excretion) properties of drug candidates is an important stage in drug discovery. To speed up the numerous tests carried out on large databases of compounds, the help of robust and accurate in silico filters is increasingly required. We propose here a method to build predictive and interpretable models for the prediction of cytochrome P450 (CYP) 1A2 and 2D6 inhibition using recursive partitioning (RP), a well-known technique for the construction of decision trees. The originality of the work is the use of several descriptions of the molecules in terms of fragments, i.e. the MACCS keys and five in-house fingerprints based on the electron density properties of fragments, employed to draw easily understandable structure-activity models. The classifiers reached performances of 87.5% and 76.5% of prediction on a validation set for CYP1A2 and CYP2D6, respectively. The analysis of the first nodes of the RP trees permits us to highlight some relations between the structural fragments and the inhibition of CYPs.

UR - http://www.scopus.com/inward/record.url?scp=67949099093&partnerID=8YFLogxK

U2 - 10.1080/10629360902726650

DO - 10.1080/10629360902726650

M3 - Article

VL - 20

SP - 185

EP - 205

JO - SAR and QSAR in environmental research

JF - SAR and QSAR in environmental research

SN - 1062-936X

ER -