Aug 2026· Bioinformatics· Vol 42· 0 citations· 36 references
Medicine
TL;DR
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.
Abstract
Abstract Motivation Molecular property prediction is a fundamental task in drug discovery and plays a key role in accelerating the identification of promising candidates. Existing methods have primarily focused on atom–bond information for molecular representation learning. However, atoms and bonds are often not explicitly organized as coupled learnable entities, and prior knowledge such as functional motifs and global molecular attributes remains largely underutilized. In addition, they lack effective mechanisms to enable interaction and fusion across different structural granularities. Consequently, the resulting representations often capture only partial chemical semantics. Results To address these issues, we propose HMG-MRL, a unified framework for hierarchical multi-granularity molecular representation learning. This framework systematically integrates domain knowledge across three granularities: fine-grained atom–bond information, medium-grained functional motifs, and coarse-grained molecular attributes. Specifically, we introduce 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. In parallel, we integrate multiple substructure decomposition strategies to construct a diverse motif vocabulary and design a Motif Transformer that employs self-attention to capture global interactions among motifs. Moreover, we design a cross-granularity communication module to facilitate information exchange across granularities. Extensive experiments on nine public benchmark datasets show that HMG-MRL achieves competitive predictive performance. Case studies further indicate that the model can reveal key molecular components across different granularities, with its diverse substructure decomposition strategies offering complementary structural patterns. Availability and implementation The data and code of HMG-MRL are available at https://github.com/AKZstar/HMG-MRL.
This is the first method to expose GNN-derived attributions to an LLM as evidence for property prediction, and achieves the best overall results among generalist models and narrows the gap to specialist models tuned for each task.
Junwoo Park, Minyoung Shin, C. Lee et al.· 0 citations
FragBERTa is introduced, a molecular fragment-aware transformer-based representation language model pretrained using masked language modeling on Sequential Attachment-based Fragment Embedding (SAFE) representations, suggesting that fragment-based string representations offer advantages over atom-level representations for scaffold-sensitive and interaction-driven tasks.
Molecular property prediction is a critical task in accelerating drug discovery. While deep learning has shown promise, prevailing single-modal methods struggle to integrate multi-source (e.g., atomic graph and molecular fingerprints), heterogeneous chemical knowledge, thereby failing to holistically represent molecular structures and capture the high-order synergistic interactions governing their functions. To address these challenges, we present HyperMolFusion, a hypergraph-enhanced multi-modal fusion model for molecular property prediction. Compared with traditional graphs limited to pairwise atomic bonds, HyperMolFusion models chemical motifs as hyperedges to explicitly capture high-order structural correlations and encode complex molecular interactions. The framework comprises three core representation learning modules: AtomConv for local atomic interaction learning via attention-enhanced message passing, HyperConv for motif-level high-order correlation extraction via hypergraph convolution with GRU gating, and a mixed molecular fingerprint module that adaptively integrates MACCS, PubChem, and Pharmacophore fingerprints. A chemically guided attention (CGA) mechanism then dynamically fuses these multi-level features into hierarchical molecular representations, alleviating over-smoothing and preserving structural information effectively. Evaluated on eight MoleculeNet benchmarks (covering regression and classification tasks), HyperMolFusion achieves promising performance. For regression, it achieves an RMSE of 0.611 in lipophilicity, 0.653 in ESOL, and 0.951 in FreeSolv. For classification, it achieves a ROC-AUC of 0.935 in ClinTox, 0.907 in BBBP, and 0.689 in SIDER. This work provides a systematic and effective solution for molecular property prediction: by holistically integrating atomic, motif, and global fingerprint information via hypergraph modeling, HyperMolFusion offers a more reliable computational tool to enhance the efficiency and accuracy of drug development pipelines.
Yawen Lin, Sheng Lian, Shaoxin Bian et al.· IEEE journal of biomedical a...· 0 citations
Computer-aided drug discovery has substantially accelerated modern pharmaceutical research, where accurate molecular property prediction plays a central role in identifying promising therapeutic candidates. Self-supervised learning (SSL), which exploits large-scale unlabeled molecular data to learn transferable representations, has recently emerged as a powerful paradigm well-aligned with the data characteristics of cheminformatics. Integrating chemical domain knowledge further enhances the ability of SSL models to capture structural, physicochemical, and functional properties of molecules. In this review, we provide a systematic overview of recent advances in SSL-based molecular property prediction. We summarize representative methodological developments and analyze how multimodal molecular representation learning─by integrating sequence, graph, three-dimensional structure, and textual information─can improve the quality and expressiveness of molecular representations. We further examine the synergistic relationship between multimodal modeling and SSL, highlighting how complementary modalities can enhance representation learning in low-label settings. To demonstrate the practical benefits of multimodal molecular properties, we compare their performance with conventional SSL models on two downstream benchmark tasks with distinct prediction objectives. Finally, we discuss key open challenges, including the scarcity of high-quality 3D molecular data, modality imbalance across data sets, and the limited interpretability of learned representations. We conclude by outlining promising research directions toward more robust, generalizable, and biologically meaningful frameworks for molecular property prediction.
Shuning Yang, Lei Deng· Journal of Chemical Informat...· 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
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.
Yu Liu, Jonathan D. Hirst, Jianfeng Ren et al.· Annual International Compute...· 0 citations