To achieve potential alternatives for hyperuricemia therapeutics, a novel structure-docking energy relationship model was established for high-throughput screening inhibitors of xanthine oxidase (XO). Molecular docking was performed between XO and 311 natural compounds from 6 traditional Chinese herbs. Then, structure-docking energy relationship model was simulated between molecular docking energy and 63 molecular descriptors by multiple linear regressions (MLR), principal component regression (PCR), and artificial neural network (ANN), respectively. The results showed that the ANN model was the best model to predict the docking energy of XO with the coefficient of determination (R2) and mean squared error (MSE) at 0.8746 and 0.9414, respectively. The data of XO inhibitory activity were consistent with the prediction in vitro, which was also further confirmed by hyperuricemia cell model. The results suggested that the structure-docking energy relationship model provides a paradigm framework for the screening of XO inhibitors.
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