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首页> 外文期刊>Particulate Science and Technology: An International Journal >Prediction of the Partition Coefficients of Biomolecules in Polymer-Polymer Aqueous Two-Phase Systems Using the Artificial Neural Network Model
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Prediction of the Partition Coefficients of Biomolecules in Polymer-Polymer Aqueous Two-Phase Systems Using the Artificial Neural Network Model

机译：人工神经网络模型预测聚合物-聚合物水两相系统中生物分子的分配系数

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摘要

In this work, an artificial neural network model was used to obtain the partition coefficients of biomolecules in polymer-polymer aqueous two-phase systems. In the artificial neural network, the partition coefficient of a biomolecule depends on the difference between concentrations of poly (ethylene glycol), dextran in the top and bottom phases, temperature and molecular weights of poly (ethylene glycol), dextran, and the biomolecule. The network topology is optimized and the (6-1-1) architecture is found using optimization of an objective function with sequential quadratic programming (SQP) method for 450 experimental data points. The results obtained from the neural network of the partition coefficients of biomolecules in polymer-polymer aqueous two-phase systems were compared with those from the modified Flory-Huggins model. Comparisons showed that the artificial neural network is 50% more accurate than the Flory-Huggins model in obtaining partition coefficients of biomolecules in polymer-polymer aqueous two-phase systems.
机译：在这项工作中，使用人工神经网络模型来获取聚合物-聚合物水两相系统中生物分子的分配系数。在人工神经网络中，生物分子的分配系数取决于上，下相中的聚乙二醇，右旋糖酐浓度，聚乙二醇，右旋糖酐和生物分子的温度和分子量之间的差异。优化了网络拓扑，并使用目标函数的优化和顺序二次编程（SQP）方法对450个实验数据点进行了优化，找到了（6-1-1）架构。将神经网络中聚合物-聚合物水两相系统中生物分子分配系数的结果与改进的Flory-Huggins模型的结果进行了比较。比较表明，在获得聚合物-聚合物水两相系统中生物分子的分配系数时，人工神经网络比Flory-Huggins模型的准确性高50％。

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