A molecular property prediction method fusing graph neural networks,gated cyclic units and attention mechanisms
Molecular property prediction has a wide range of applications in drug research and development.Although graph neural networks and other methods have been applied to predict molecular properties,there are still limitations in processing large-scale molecular maps and information dissemination.To solve this problem,a network model was constructed fusing graph neural network(GNN),gated circulation unit(GRU)and attention mechanism(GAGCN)in this paper to predict molecular properties.The model used GNN to represent and learn molecular graphs,and the connections between nodes and information propagation was used to capture molecular structural features.The GRU was used to model the molecular sequence,so that the timing information in the molecular sequence was captured,and the information in the sequence was adapted to retain or discard through the gating mechanism.Finally,the attention mechanism was used to learn the weights between different features,and the GNN and GRU were integrated,so that the model can make full use of the molecular structure and sequence information to improve the accuracy of molecular property prediction.The experimental results showed that the prediction accuracy of MSE,MAE and R2 for LogP attributes were 0.0010,0.0116 and 0.9993,respectively.The model proposed in this paper provides technical support and reference for the research and development of new pesticides and new veterinary drugs.
drug developmentmolecular property predictiongraph neural networkgated loop unitattention mechanism
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