Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow, 226 001, India.
Environ Sci Pollut Res Int. 2015 Aug;22(16):12699-710. doi: 10.1007/s11356-015-4526-3. Epub 2015 Apr 28.
Safety assessment and designing of safer ionic liquids (ILs) are among the priorities of the chemists and toxicologists today. Computational approaches have been considered as appropriate methods for prior safety assessment of chemicals and tools to aid in structural designing. The present study is an attempt to investigate the chemical attributes of a wide variety of ILs towards their cytotoxicity in leukemia rat cell line IPC-81 through the development of nonlinear quantitative structure-activity relationship (QSAR) models in the light of the OECD principles for QSAR development. Here, the cascade correlation network (CCN), probabilistic neural network (PNN), and generalized regression neural networks (GRNN) QSAR models were established for the discrimination of ILs in four categories of cytotoxicity and their end-point prediction using few simple descriptors. The diversity and nonlinearity of the considered dataset were evaluated through computing the Euclidean distance and Brock-Dechert-Scheinkman statistics. The constructed QSAR models were validated with external test data. The predictive power of these models was established through a variety of stringent parameters recommended in QSAR literature. The classification QSARs rendered the accuracy of >86%, and the regression models yielded correlation (R(2)) of >0.90 in test data. The developed QSAR models exhibited high statistical confidence and identified the structural elements of the ILs responsible for their cytotoxicity and, hence, could be useful tools in structural designing of safer and green ILs.
安全评估和更安全的离子液体（ILs）的设计是当今化学家及毒理学家的重点研究方向之一。计算方法已被认为是化学品预先安全评估的合适方法，也是辅助结构设计的工具。本研究旨在通过基于 OECD QSAR 发展原则，开发非线性定量构效关系（QSAR）模型，来研究各种 ILs 的化学性质与其对白血病大鼠细胞系 IPC-81 的细胞毒性之间的关系。在此，通过计算欧几里得距离和 Brock-Dechert-Scheinkman 统计量，评估了所考虑数据集的多样性和非线性。使用少数简单描述符，建立了用于区分 ILs 的四种细胞毒性类别和终点预测的级联相关网络（CCN）、概率神经网络（PNN）和广义回归神经网络（GRNN）QSAR 模型。通过 QSAR 文献中推荐的各种严格参数，建立了这些模型的预测能力。分类 QSAR 的准确率>86%，回归模型在测试数据中的相关性（R(2)）>0.90。所开发的 QSAR 模型具有较高的统计置信度，并确定了 ILs 的结构元素，这些结构元素与其细胞毒性有关，因此，它们可能是设计更安全和绿色的 ILs 的有用工具。
