Liu Huanxiang, Gramatica Paola
QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology, University of Insubria, via Dunant 3, Varese, Italy.
Bioorg Med Chem. 2007 Aug 1;15(15):5251-61. doi: 10.1016/j.bmc.2007.05.016. Epub 2007 May 10.
In this paper, an accurate and reliable QSAR model of 87 selective ligands for the thyroid hormone receptor beta 1 (TRbeta1) was developed, based on theoretical molecular descriptors to predict the binding affinity of compounds with receptor. The structural characteristics of compounds were described wholly by a large amount of molecular structural descriptors calculated by DRAGON. Six most relevant structural descriptors to the studied activity were selected as the inputs of QSAR model by a robust optimization algorithm Genetic Algorithm. The built model was fully assessed by various validation methods, including internal and external validation, Y-randomization test, chemical applicability domain, and all the validations indicate that the QSAR model we proposed is robust and satisfactory. Thus, the built QSAR model can be used to fast and accurately predict the binding affinity of compounds (in the defined applicability domain) to TRbeta1. At the same time, the model proposed could also identify and provide some insight into what structural features are related to the biological activity of these compounds and provide some instruction for further designing the new selective ligands for TRbeta1 with high activity.
在本文中，基于理论分子描述符开发了一种针对甲状腺激素受体β1（TRbeta1）的87种选择性配体的准确可靠的定量构效关系（QSAR）模型，以预测化合物与受体的结合亲和力。化合物的结构特征通过由DRAGON计算的大量分子结构描述符进行全面描述。通过稳健的优化算法遗传算法选择与所研究活性最相关的六个结构描述符作为QSAR模型的输入。所构建的模型通过各种验证方法进行全面评估，包括内部和外部验证、Y随机化测试、化学适用性域，所有验证均表明我们提出的QSAR模型是稳健且令人满意的。因此，所构建的QSAR模型可用于快速准确地预测化合物（在定义的适用性域内）与TRbeta1的结合亲和力。同时，所提出的模型还可以识别并深入了解与这些化合物的生物活性相关的结构特征，并为进一步设计具有高活性的新型TRbeta1选择性配体提供一些指导。
