Roy Kunal, Popelier Paul L A
Manchester Interdisciplinary Biocenter (MIB), 131 Princess Street, Manchester, M1 7DN, United Kingdom.
Bioorg Med Chem Lett. 2008 Apr 15;18(8):2604-9. doi: 10.1016/j.bmcl.2008.03.035. Epub 2008 Mar 16.
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.
我们使用量子拓扑分子相似性（QTMS）描述符以及疏水性（logP）作为预测变量，构建了酚类肝细胞毒性数据的预测定量构效关系（QSAR）模型。QTMS描述符在不同理论水平下进行计算，包括AM1、HF/3 - 21G(d)、HF/6 - 31G(d)、B3LYP/6 - 31 + G(d,p)、B3LYP/6 - 311 + G(2d,p)和MP2/6 - 311 + G(2d,p)。在较高理论水平下最佳模型的外部预测能力高于较低理论水平下的模型。此外，最佳的QTMS模型在外部预测能力方面优于使用pK(a)、哈米特sigma(+)以及logP的偏最小二乘（PLS）模型。当前研究表明在QSAR研究中，量子化学衍生的描述符相对于物理化学（实验衍生或表格形式）电子描述符具有优势。
