Enhancing Molecular Property Prediction with Gaussian-Enhanced Graph Matching

In recent years, graph neural network technology has revolutionized molecular graph matching methods, offering new opportunities for drug discovery. Central to this research are two key steps: latent representation learning of molecules and molecular graph matching. However, the challenge lies in effectively capturing and expressing the intricate features of molecular graphs, given their strict chemical rules and high structural complexity. To address this, we propose a gaussian-enhanced graph matching method. This approach combines a dual-channel message-passing neural network, based in the GIN algorithm, to encode both nodes and edges of molecules, enabling precise representations of molecular graphs. Additionally, an end-to-end graph similarity calculation model is introduced, assessing similarity at both the node and global levels. By integrating these evaluations, we obtain a comprehensive similarity score for molecular graph pairs. This method enhances the accuracy and interpretability of molecular structural similarity. Experimental results demonstrate the superior performance of our model compared to state-of-the-art baseline methods, marking a significant step forward in molecular property prediction.