Interaction Energy Prediction of Organic Molecules using Deep Tensor Neural Network

• 作 者: Yuan Qi Hong Ren Hong Li Ding-lin Zhang Hong-qiang Cui Jun-ben Weng Guo-hui Li Gui-yan Wang Yan Li
• 机 构: Dalian Institute of Chemical Physics,State Key Laboratory of Molecular Reaction Dynamics,Dalian 1160
• 出 处: 化学物理学报（英文版）
• 发表日期: 2021年1期
• 关键词: Deep tensor neural network Interaction energy Organic molecules

The interaction energy of two molecules system plays a critical role in analyzing the interacting effect in molecular dy-namic simulation.Since the limitation of quan-tum mechanics calculating resources,the interaction energy based on quantum mechanics can not be merged into molecular dynamic simulation for a long time scale.A deep learning framework,deep tensor neural network,is applied to predict the interaction energy of three organic related systems within the quantum mechanics level of accuracy.The geometric structure and atomic types of molecular conformation,as the data descriptors,are applied as the network inputs to predict the interaction energy in the system.The neural network is trained with the hierarchically generated conformations data set.The complex tensor hidden layers are simplified and trained in the optimization process.The predicted results of different molecular systems indicate that deep tensor neural network is capable to predict the interaction energy with 1 kcal/mol of the mean absolute error in a relatively short time.The prediction highly improves the efficiency of interaction energy calculation.The whole proposed framework provides new insights to introducing deep learning technology into the interaction energy calculation.