Due to the important application of molecular structure in many fields, calculation by experimental means or traditional density functional theory is often time consuming. In view of this, a new Model Structure based on Graph Convolutional Neural network (MSGCN) is proposed, which can determine the molecular structure by predicting the distance between two atoms. In order to verify the effect of MSGCN model, the model is compared with the method of calculating molecular three-dimensional conformation in RDKit, and the result is better than it. In addition, the distance predicted by the MSGCN model and the distance calculated by the QM9 dataset were used to predict the molecular properties, thus proving the effectiveness of the distance predicted by the MSGCN model.
翻译:由于分子结构在许多领域的重要应用,用实验手段或传统密度功能理论进行计算往往耗费时间,因此,提议以图变神经网络(MSGCN)为基础建立一个新的模型结构,通过预测两个原子之间的距离来确定分子结构,为了核实MSGCN模型的效果,该模型与RDKit的分子三维相容计算方法进行了比较,其结果比它好。此外,还利用MSGCN模型预测的距离和QM9数据集计算的距离来预测分子特性,从而证明MSGCN模型预测的距离的有效性。