Construction and Performance Evaluation of Organic Molecule Property Prediction Model Based on Graph Neural Network
The rapid and accurate prediction of organic molecule properties is a key issue in drug screening, functional material design, and chemical reaction optimization. Due to the problem that traditional molecular descriptors rely on manual feature engineering and are difficult to fully express the topological structure of molecules, this paper constructs an organic molecule property prediction model based on graph neural networks. The molecule is represented as a graph structure composed of atomic nodes and chemical bond edges, and the local chemical environment and global molecular representation are learned through the message passing mechanism. Experiments selected three typical molecular property prediction tasks of ESOL, FreeSolv, and Lipophilicity, and compared with models such as random forest, support vector regression, multi-layer perceptron, GCN, GAT, and MPNN. The results show that the proposed attention-enhanced graph neural network model achieves the best or near-best performance on all three datasets. Specifically, the test set RMSE of the ESOL dataset drops to 0.52, and R2 increases to 0.86; the RMSE of the FreeSolv dataset drops to 1.08, and R2 reaches 0.83; the RMSE of the Lipophilicity dataset drops to 0.57, and R2 reaches 0.78. The ablation experiments further indicate that by introducing edge features, attention reading mechanism, and molecule descriptor fusion, the model prediction error is reduced by 6.8%, 5.4%, and 4.7% respectively. The research results show that graph neural networks can effectively capture the structure-property relationship of organic molecules, providing an effective method for rapid prediction of molecular properties and computer-aided molecular design.