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

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    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 languageEnglish
    Pages (from-to)2604-2609
    Number of pages5
    JournalBioorganic and Medicinal Chemistry Letters
    Issue number8
    Publication statusPublished - 15 Apr 2008


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


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