Dept. of Environmental Science and Engineering, College of Engineering, Center for Environmental Studies, Kyung Hee University, Seocheon-dong 1, Giheung-gu, Yongin-Si, Gyeonggi-Do 446-701, Republic of Korea.
Nano Drug Delivery Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Ecotoxicol Environ Saf. 2018 Oct 30;162:17-28. doi: 10.1016/j.ecoenv.2018.06.061. Epub 2018 Jun 26.
Octanol/water partition coefficient (log P), octanol/air partition coefficient (log K) and bioconcentration factor (log BCF) are important physiochemical properties of organic substances. Quantitative structure-property relationship (QSPR) models are a promising alternative method of reducing and replacing experimental steps in determination of log P, log K and log BCF. In the current study, we propose a new QSPR model based on a deep belief network (DBN) to predict the physicochemical properties of polychlorinated biphenyls (PCBs). The prediction accuracy of the proposed model was compared to the results of previous reported models. The predictive ability of the DBN model, validated with a test set, is clearly superior to the other models. All results showed that the proposed model is robust and satisfactory, and can effectively predict the physiochemical properties of PCBs without highly reliable experimental values.
辛醇/水分配系数（log P）、辛醇/空气分配系数（log K）和生物浓缩因子（log BCF）是有机物质的重要物理化学性质。定量构效关系（QSPR）模型是一种很有前途的替代方法，可以减少和替代实验步骤来确定 log P、log K 和 log BCF。在本研究中，我们提出了一种基于深度置信网络（DBN）的新 QSPR 模型，用于预测多氯联苯（PCBs）的物理化学性质。与以前报道的模型相比，我们对所提出模型的预测精度进行了比较。该 DBN 模型的预测能力，通过测试集进行验证，明显优于其他模型。所有结果表明，所提出的模型是稳健和令人满意的，可以有效地预测 PCBs 的物理化学性质，而无需高度可靠的实验值。
