Suppr超能文献

使用二维描述符对大型溞急性毒性的有机染料的定量构效关系建模。

QSAR modelling of organic dyes for their acute toxicity in Daphnia magna using 2D-descriptors.

机构信息

Department of Pharmacoinformatics, National Institute of Pharmaceutical Educational and Research (NIPER), Kolkata, India.

Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India.

出版信息

SAR QSAR Environ Res. 2022 Feb;33(2):111-139. doi: 10.1080/1062936X.2022.2033318.

Abstract

The present study reports quantitative structure-activity relationship (QSAR) models for 22 organic dyes spanning a broad chemical domain to predict their toxicity in [log (1/EC)]. Only two-dimensional descriptors with clear physicochemical meaning were used to construct the QSAR models. The process of development, validation, and interpretation of models adheres to the stringent recommendations of the Organization for Economic Cooperation and Development (OECD) guidelines. In this study, the multi-layered stepwise regression method and linear discriminant analysis (LDA) method were employed for the deployment of regression - and classification-based models respectively; however, the final regression-based QSAR models were obtained through the partial least squares (PLS) regression. Additionally, the applicability domain of the developed models was verified. The constructed models should be applicable in the absence of toxicity data of new or untested dye structures, particularly when the compounds fall within the developed models' scope, and also implementable to develop more environmentally friendly alternatives.

摘要

本研究报告了 22 种有机染料的定量构效关系 (QSAR) 模型,这些染料涵盖了广泛的化学领域,可用于预测其在 [log (1/EC)] 中的毒性。仅使用具有明确物理化学意义的二维描述符来构建 QSAR 模型。模型的开发、验证和解释过程均遵循经济合作与发展组织 (OECD) 指南的严格建议。在本研究中,采用多层逐步回归法和线性判别分析 (LDA) 分别用于部署回归和分类模型;然而,最终的回归 QSAR 模型是通过偏最小二乘 (PLS) 回归获得的。此外,还验证了所建立模型的适用性域。所开发的模型应适用于缺乏新的或未经测试的染料结构的毒性数据的情况,特别是当化合物处于所开发模型的范围内时,并且还可用于开发更环保的替代品。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验