3 years ago

In silico prediction of compounds binding to human plasma proteins by QSAR models

Guixia Liu, Jie Li, Tianduanyi Wang, Lixia Sun, Yun Tang, Weihua Li, Hongbin Yang
Plasma protein binding (PPB) is a significant pharmacokinetic property of compounds in drug discovery and design. Due to experimental assays being high-cost and time-consuming, in silico approaches are developed for assessing the binding profiles of chemicals. However, because of lacking unambiguous and uniform experimental data, most of predictive models available are far from satisfactory. In this study, an elaborately curated training set containing 967 diverse pharmaceuticals with plasma protein bound fraction (fb) was utilized to construct quantitative structure-activity relationship (QSAR) models by six machine learning algorithms with 26 molecular descriptors. Furthermore, we combined all the individual learners to yield consensus prediction and the accuracy of the consensus model was marginally improved. The model performance was estimated by 10-fold cross validation and three external validation sets comprised of 242 pharmaceutical, 397 industrial, and 231 newly designed chemicals, respectively. The models showed excellent performance for the whole test set with mean absolute error (MAE) ranging from 0.126 to 0.178, demonstrating that our models could be used by a chemist when drawing a molecular structure from scratch. Meanwhile, structural descriptors contributing significantly to the predictive power of the models were related to the binding mechanisms, and the trends in term of their effects on PPB can serve as a guidance for structural modification of chemicals.

Publisher URL: http://onlinelibrary.wiley.com/resolve/doi

DOI: 10.1002/cmdc.201700582

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