Jul 2026· Annual International Computer Software and Applications Conference· pp. 2525-2530· 0 citations· 26 references
Abstract
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.
Molecular property prediction is a cornerstone for accelerating drug discovery, providing a computational way to alleviate the low success rate, high cost, and long development cycle of conventional pharmaceutical research and development. However, existing computational methods have obvious limitations: most current deep learning approaches rely on a single molecular view, failing to fully capture the multi-dimensional features of molecular structures; they mainly focus on intra-molecular features while ignoring inter molecular information and cross-task correlations; and limited labeled data severely impairs their generalization to novel molecules. To address these issues, we propose MSMPP, a multi-scale, multi-view fusion framework for molecular property prediction that learns intra- and inter scale features simultaneously. For intra-scale feature learning, MSMPP integrates TxGemma-enhanced 1D sequence representations, Graph Transformer-derived 2D topological graph features, and Uni-Mol-derived 3D molecular conformational features. The Graph Transformer models long-range atomic dependencies, while the two pretrained models provide task-agnostic molecular prior knowledge from large-scale pretraining corpora, thereby improving generalization to novel molecules. For inter-scale feature learning, MSMPP constructs an inter-molecular graph (IMG) that explicitly models global pairwise interactions among chemically similar molecules and also extracts the cross-task features. Evaluations on eight MoleculeNet datasets show that MSMPP significantly outperforms state-of-the-art models, demonstrating its effectiveness in integrating multi-view intra-molecular features, inter-molecular features and cross-task information. Overall, MSMPP provides a competitive tool for molecular property prediction and supports the acceleration of drug discovery workflows.
Jiongfeng Chen, Yulian Ding, Yan Yan et al.· IEEE journal of biomedical a...· 0 citations
A novel Dual-Attention Multimodal framework for Graphs and Sequence-based representations, so-called DAM-GS, which provides a promising solution for molecular property prediction with broad applications in drug discovery and computational molecular science.
Bay Van Nguyen, Vinh Truong, Ha Duong Thi Hong et al.· Journal of Chemical Informat...· 0 citations
An atom–bond bipartite graph modeling approach that treats atoms and bonds as explicit learnable node types and jointly models atom–atom, atom–bond, and bond–bond local interactions within a unified propagation framework is introduced.
Xing Zhao, Xianlai Chen, Yunbo Wang et al.· Bioinformatics· 0 citations
Accurate prediction of drug-target binding affinity (DTA) is a key task in virtual screening. However, current computational methods face a key challenge: sequence-based approaches often fail to capture critical spatial information, while structure-based models rely on computationally expensive 3D coordinates, which restrict their scalability. To address this issue, we propose StructuraDTA, a novel multimodal framework that adopts an implicit structure modeling strategy. Instead of using static protein folding data, our method encodes drug molecular graphs via Graph Isomorphism Networks (GINs) to capture fine-grained topological features. Meanwhile, we optimize protein representations by integrating probabilistic structural priors into a pretrained language model, which effectively simulates thermodynamic conformational flexibility without relying on explicit 3D structural data. A bidirectional cross-attention mechanism is then used to dynamically align these heterogeneous feature modalities. Comprehensive evaluations on the Davis and KIBA benchmark datasets show that StructuraDTA stably outperforms state of-the-art comparison methods. Importantly, the model exhibits strong robustness in cold-start scenarios, and can accurately predict binding affinities for previously unseen drugs and targets. By retaining the predictive performance of structure based models while maintaining the high inference efficiency of sequence-based methods, we provide an accurate and scalable solution to accelerate genome-scale drug discovery research.
Junlin Xu, Ye Yuan, Menglong Hu et al.· IEEE journal of biomedical a...· 0 citations
Reliable structure-property modeling is crucial for accelerating materials discovery, where crystal graphs and structure-derived crystallographic descriptions provide complementary geometric and semantic information. Existing multimodal materials models primarily incorporate textual information through post-encoding fusion, latent-space alignment, or attention-based representation interaction mechanisms. However, in most cases, crystallographic semantics are introduced after structural encoding and therefore cannot directly guide the formation of atom-level crystal-graph representations. Here, we present Semantics-Augmented Geometric Encoder Network (SAGE-Net), a flexible multimodal framework that injects description-derived chemical and crystallographic semantics into geometric message passing. SAGE-Net introduces Semantic-Guided Message Passing (SGMP), which gates atom-level updates and enables crystallographic semantics to directly modulate local geometric interactions across multiple graph neural network (GNN) backbones. Across benchmarks covering bandgap, mechanical, transport-related properties, and synthesizability assessment, the SAGE-Net instantiated with different GNN backbones achieves the lowest MAE on eight out of ten JARVIS-DFT regression targets and delivers strong or highly competitive performance against both structure-based and multimodal baselines. For synthesizability assessment, the SAGE-Net demonstrate outstanding classification performance and high recall rates. Interpretability analysis unravels that SAGE-Net effectively captures physically interpretable crystallographic features, viz. space group, dimensionality, polyhedral environments, among others. Together, these results demonstrate SGMP-based SAGE-Net as a general and transferable framework for deeply integrated multimodal materials learning.
Guanghui Zhang, Yuxuan Yao, Kieran B. Spooner et al.· 0 citations
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.
Phu Pham· International Conference on...· 0 citations