Exploring predictive QSAR models for hepatocyte toxicity of phenols using QTMS descriptors

Kunal Roy; Paul L A Popelier

doi:10.1016/j.bmcl.2008.03.035

Exploring predictive QSAR models for hepatocyte toxicity of phenols using QTMS descriptors

Research output: Contribution to journal › Article › peer-review

Abstract

We construct predictive QSAR models for hepatocyte toxicity data of phenols using Quantum Topological Molecular Similarity (QTMS) descriptors along with hydrophobicity (log P) as predictor variables. The QTMS descriptors were calculated at different levels of theory including AM1, HF/3-21G(d), HF/6-31G(d), B3LYP/6-31+G(d,p), B3LYP/6-311+G(2d,p) and MP2/6-311+G(2d,p). The external predictability of the best models at the higher levels of theory is higher than that at the lower levels. Moreover, the best QTMS models are better in external predictability than the PLS models using pKa and Hammett σ+ along with log P. The current study implies the advantage of quantum chemically derived descriptors over physicochemical (experimentally derived or tabular) electronic descriptors in QSAR studies. © 2008 Elsevier Ltd. All rights reserved.

Original language	English
Pages (from-to)	2604-2609
Number of pages	5
Journal	Bioorganic and Medicinal Chemistry Letters
Volume	18
Issue number	8
DOIs	https://doi.org/10.1016/j.bmcl.2008.03.035
Publication status	Published - 15 Apr 2008

Keywords

Ab initio
Atoms in molecules
Electron density
External validation
Phenols
QSAR
QTMS
Quantum chemical topology
Toxicity

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10.1016/j.bmcl.2008.03.035

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title = "Exploring predictive QSAR models for hepatocyte toxicity of phenols using QTMS descriptors",

abstract = "We construct predictive QSAR models for hepatocyte toxicity data of phenols using Quantum Topological Molecular Similarity (QTMS) descriptors along with hydrophobicity (log P) as predictor variables. The QTMS descriptors were calculated at different levels of theory including AM1, HF/3-21G(d), HF/6-31G(d), B3LYP/6-31+G(d,p), B3LYP/6-311+G(2d,p) and MP2/6-311+G(2d,p). The external predictability of the best models at the higher levels of theory is higher than that at the lower levels. Moreover, the best QTMS models are better in external predictability than the PLS models using pKa and Hammett σ+ along with log P. The current study implies the advantage of quantum chemically derived descriptors over physicochemical (experimentally derived or tabular) electronic descriptors in QSAR studies. {\textcopyright} 2008 Elsevier Ltd. All rights reserved.",

keywords = "Ab initio, Atoms in molecules, Electron density, External validation, Phenols, QSAR, QTMS, Quantum chemical topology, Toxicity",

author = "Kunal Roy and Popelier, {Paul L A}",

year = "2008",

month = apr,

day = "15",

doi = "10.1016/j.bmcl.2008.03.035",

language = "English",

volume = "18",

pages = "2604--2609",

journal = "Bioorganic and Medicinal Chemistry Letters",

issn = "1464-3405",

publisher = "Elsevier BV",

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TY - JOUR

T1 - Exploring predictive QSAR models for hepatocyte toxicity of phenols using QTMS descriptors

AU - Roy, Kunal

AU - Popelier, Paul L A

PY - 2008/4/15

Y1 - 2008/4/15

N2 - We construct predictive QSAR models for hepatocyte toxicity data of phenols using Quantum Topological Molecular Similarity (QTMS) descriptors along with hydrophobicity (log P) as predictor variables. The QTMS descriptors were calculated at different levels of theory including AM1, HF/3-21G(d), HF/6-31G(d), B3LYP/6-31+G(d,p), B3LYP/6-311+G(2d,p) and MP2/6-311+G(2d,p). The external predictability of the best models at the higher levels of theory is higher than that at the lower levels. Moreover, the best QTMS models are better in external predictability than the PLS models using pKa and Hammett σ+ along with log P. The current study implies the advantage of quantum chemically derived descriptors over physicochemical (experimentally derived or tabular) electronic descriptors in QSAR studies. © 2008 Elsevier Ltd. All rights reserved.

AB - We construct predictive QSAR models for hepatocyte toxicity data of phenols using Quantum Topological Molecular Similarity (QTMS) descriptors along with hydrophobicity (log P) as predictor variables. The QTMS descriptors were calculated at different levels of theory including AM1, HF/3-21G(d), HF/6-31G(d), B3LYP/6-31+G(d,p), B3LYP/6-311+G(2d,p) and MP2/6-311+G(2d,p). The external predictability of the best models at the higher levels of theory is higher than that at the lower levels. Moreover, the best QTMS models are better in external predictability than the PLS models using pKa and Hammett σ+ along with log P. The current study implies the advantage of quantum chemically derived descriptors over physicochemical (experimentally derived or tabular) electronic descriptors in QSAR studies. © 2008 Elsevier Ltd. All rights reserved.

KW - Ab initio

KW - Atoms in molecules

KW - Electron density

KW - External validation

KW - Phenols

KW - QSAR

KW - QTMS

KW - Quantum chemical topology

KW - Toxicity

U2 - 10.1016/j.bmcl.2008.03.035

DO - 10.1016/j.bmcl.2008.03.035

M3 - Article

SN - 1464-3405

VL - 18

SP - 2604

EP - 2609

JO - Bioorganic and Medicinal Chemistry Letters

JF - Bioorganic and Medicinal Chemistry Letters

IS - 8

ER -

Exploring predictive QSAR models for hepatocyte toxicity of phenols using QTMS descriptors

Abstract

Keywords

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