Multi‐Viewed Graph Representation Learning Through Graph Neural Network and Rich‐Spatial Local Feature Embedding

Abstract

For many years, graph representation learning plays a pivotal role in bioinformatics and cheminformatics; as a result, supporting a wide range of tasks such as drug discovery, toxicity prediction, and compound–protein interaction analysis. However, existing approaches often focus solely on either sequential molecular fingerprints or graph‐based structural features, which limit their ability to capture both local chemical substructures and global molecular topology. To address this issue, we propose MM2Vec, a novel multi‐viewed molecular representation learning framework that integrates local rich‐feature embedding with graph neural network (GNN)‐based structural learning. Specifically, each molecular graph is first processed through an MLP‐based embedding layer that encodes sub‐structural fingerprint information extracted from radius‐based subgraphs, capturing fine‐grained chemical and physiochemical features. Simultaneously, a multi‐layered GNN encoder learns topological relationships from the molecular graph structure; therefore, focusing more on geometric and relational information among atoms. The outputs from both embedding branches are then fused using a learnable linear mechanism to produce unified, high‐quality molecular embeddings in a shared latent space. These fused representations are used to drive task‐specific prediction layers for addressing various learning objectives. We validate the proposed MM2Vec model on multiple graph learning tasks, including drug‐induced liver injury (DILI) classification and lethal dose (LD) molecular regression problems. Experimental results show that MM2Vec consistently outperforms classical machine learning (ML)‐based models and recent state‐of‐the‐art deep learning (DL)/GNN‐based methods in terms of accuracy, robustness, and generalization. Our findings in this highlight the importance of combining both sub‐structural and graph‐structural perspectives and demonstrate the versatility and effectiveness of our MM2Vec model for a wide range of molecular analysis tasks.