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Updated: Feb 8, 2026

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
Published on: May 9, 2025
Quantitative structure-property relationship (QSPR) models for predicting the physicochemical properties of
Usman Safder1, KiJeon Nam1, Dongwoo Kim1
1Dept. 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.
Abstract:
Octanol/water partition coefficient (log P), octanol/air partition coefficient (log KOA) 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 KOA 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.
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