Exploring predictive QSAR models for hepatocyte toxicity of phenols using QTMS descriptors.
We construct predictive QSAR models for hepatocyte toxicity data of phenols using Quantum Topological Molecular Similarity (QTMS) descriptors along with hydrophobicity (logP) 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 pK(a) and Hammett sigma(+) along with logP. The current study implies the advantage of quantum chemically derived descriptors over physicochemical (experimentally derived or tabular) electronic descriptors in QSAR studies.
Roy
Kunal
K
Manchester Interdisciplinary Biocenter (MIB), 131 Princess Street, Manchester, M1 7DN, United Kingdom. kroy@pharma.jdvu.ac.in
Popelier
Paul L A
PL
D022781:Hepatocytes / Q000187:drug effects*; D008954:Models, Biological; D015394:Molecular Structure; D010636:Phenols / Q000737:chemistry* / Q000633:toxicity*; D021281:Quantitative Structure-Activity Relationship
D010636:Phenols