Learning effective molecular representations is crucial for accurate property prediction in AI-aided drug discovery. However, most existing molecular pre-training methods are still primarily based on 2D topological graphs, limiting their ability to exploit 3D geometric information. Moreover, methods that do incorporate 3D geometry often do not distinguish between the roles of atom-centered and bond-centered representations. To address these limitations, we propose GDGraph, a geometryenhanced dual-view framework for molecular representation learning. GDGraph models molecular geometry from two complementary structural perspectives: an atom view for capturing global spatial dependencies and a bond view for modeling local geometric patterns. To support this dual-view design, we introduce a multi-scale geometric feature encoding scheme and a view-specific geometry-aware learning strategy, enabling each view to focus on the geometric dependencies it is best suited to capture. Extensive experiments demonstrate that GDGraph achieves strong and stable performance on molecular property prediction benchmarks, and effectively predicts geometrysensitive quantum chemical properties on the QM9 dataset.

Learning effective molecular representations is crucial for accurate property prediction in artificial intelligence (AI)-aided drug discovery. Graph and fingerprint representations have been widely used to encode molecular topological structures and chemical substructures. To enhance the feature embedding of each modality and leverage their complementary strengths, we propose a novel Chemically-aware Attention-based Multi-modal Fusion Framework (CAMFF) for molecular representation learning, which integrates molecular graphs and extended-connectivity fingerprints by exploiting various attention mechanisms. Specifically, the proposed CAMFF consists of three modules: 1) a graph embedding module incorporating multi-head attention to capture local heterogeneous interactions and all-pair self-attention to capture long-range atomic dependencies from molecular graph representations; 2) a fingerprint embedding module using a pre-trained Mol2Vec model to generate dense chemical substructure representations; and 3) a chemically-aware feature interaction and fusion module incorporating self-attention to enable interactions between various chemical substructures and cross-attention to ensure effective multi-modal alignment and fusion. To evaluate the effectiveness of CAMFF, we compare it with 14 state-of-the-art methods across 9 molecular property prediction benchmarks. CAMFF demonstrates competitive predictive performance and improves interpretability through attention-based visualization, showing its potential for real-world drug discovery.

Graph Neural Networks (GNNs) have been widely used to model molecules with a graph representation. However, GNNs face inherent challenges in accurately modeling long-range atomic interactions and identifying complex molecular substructures. This research proposes a novel Three-branch Molecular Representation Learning Framework (TMRLF) for predicting molecular properties: it integrates one branch of a GNN that extracts local molecular structural information with two branches of fully connected networks that capture the chemical substructure based on two fingerprints. Specifically, to better capture the long-range interactions, the GNN is designed with an attention mechanism to enhance the atomic interactions. As the Morgan fingerprint effectively captures functional groups of molecules and another well-used molecular fingerprint in the field of drug discovery, the Extended Reduced Graph (ErG) Fingerprint specifically targets molecular features with pharmacological relevance. These two fingerprints are both utilized to complement the chemical information and long-range information processing at the level of key structural features that GNNs lack. The proposed TMRLF extracts a robust feature representation of molecules, crucial for accurately predicting molecular properties and identifying potential drug candidates. Our proposed TMRLF is compared against six state-of-the-art models on eight benchmark datasets. It demonstrates superior capability in predicting molecular properties. Its effectiveness is further highlighted through proof-of-concept validation in identifying potential inhibitors for the Son of Sevenless Homolog 1 (SOSI) protein in real-world drug discovery scenarios.