Jul 2026· Journal of Chemical Information and Modeling· Vol 66 16, pp.
10379-10395
· 0 citations· 42 references
Medicine
TL;DR
Experiments demonstrate that HSAF-DDI achieves superior overall performance compared to state-of-the-art methods, indicating the critical role of fine-grained biological features in improving the DDI prediction performance.
Abstract
Drug-drug interactions (DDIs) are modulated not only by structural relations between drugs but are also profoundly influenced by underlying biological mechanisms. Most existing methods fail to adequately consider biologically interpretable signals at multiple biological hierarchies, limiting their ability to model the mechanistic complexity of drug-drug interactions. In this paper, we propose a heterogeneous semantic-aware framework (HSAF-DDI) that integrates molecular motif, protein sequence, and knowledge graph representations for DDI prediction. This framework learns local functional semantic representations of molecular motifs, fine-grained target protein semantics that capture deep biological characteristics, and higher-order associations encoded in knowledge graphs. We design a hierarchical adaptive fusion module that facilitates robust fusion and adaptive representation learning over multisource heterogeneous information. In addition, we introduce a contrastive learning mechanism with adversarial negatives and perturbations to improve the discrimination of DDI types and enhance the discriminability and robustness of learned representations. Experiments demonstrate that HSAF-DDI achieves superior overall performance compared to state-of-the-art methods, indicating the critical role of fine-grained biological features in improving the DDI prediction performance.
Accurate prediction of drug-drug interactions (DDIs) is crucial for medication safety and personalized treatment. Most existing methods primarily exploit molecular graphs or biomedical knowledge graphs, while target protein sequence information is often underused. This paper proposes CMAF-DDI, a multi-class DDI prediction framework that integrates protein sequence features, molecular graph features, and knowledge graph features. CMAF-DDI contains a bi-level cross-modal fusion module: an Attention Fusion (AF) level that models global dependencies among modalities using multi-head attention, and a Triple-feature Product Fusion (TPF) level that captures high-order cross-modal co-activation after projecting all modalities into a shared latent space. Experimental results on DrugBank and DRKG show that CMAF-DDI improves multi-class DDI prediction compared with representative graph-based and multi-source fusion baselines. We further provide ablation, hyperparameter sensitivity, controlled protein perturbation, representation, and case-level analyses to examine the contribution and behavior of protein-enhanced cross-modal fusion.
Hengpeng Zhao, Xiaoli Lin, Jun Pang et al.· IEEE journal of biomedical a...· 0 citations
Drug-drug interaction (DDI) prediction is an important task in computational pharmacology because unidentified interactions may reduce therapeutic efficacy or induce severe adverse effects. Although recent deep learning methods have achieved promising performance, most existing approaches primarily rely on either two-dimensional (2D) molecular topology or coarse multimodal fusion strategies, while insufficiently modeling fine-grained interaction dependencies between drug pairs. To address these limitations, we propose IAMV-DDI, an interaction-aware multi-view molecular representation learning framework for DDI prediction and DDI event classification. The proposed framework jointly integrates 2D molecular topology, 3D spatial geometry, and token-level interdrug interaction modeling. Specifically, a SimSGT-based masked graph encoder is employed to learn informative 2D molecular representations, while an E(n) Equivariant Graph Neural Network (EGNN) encoder with contrastive conformer pretraining captures geometry-aware 3D structural features. The learned 2D and 3D token representations are integrated through a gated cross-modal fusion module, followed by a bidirectional cross-attention mechanism to explicitly model interaction-aware dependencies between drug pairs. Experiments conducted on the benchmark DrugBank and ZhangDDI data sets demonstrate that IAMV-DDI achieves strong performance compared with representative network-based, chemical-structure-based, and hybrid baseline methods. In binary DDI prediction, IAMV-DDI achieves highly competitive performance on DrugBank and ZhangDDI, with closely matched results to the strongest baseline on ZhangDDI. In DrugBank multiclass DDI event classification, IAMV-DDI achieves an Accuracy of 0.9650, Macro-Precision of 0.9439, Macro-Recall of 0.9347, and Macro-F1 of 0.9361, substantially outperforming the strongest baseline. Ablation studies further confirm the effectiveness of the multiview molecular fusion strategy and the interaction-aware cross-attention mechanism. These results demonstrate that jointly modeling molecular topology, spatial geometry, and fine-grained interdrug dependencies can produce highly discriminative representations for accurate DDI prediction.
Huyen K. Nguyen, Quang H. Nguyen, D. Le· Journal of Chemical Informat...· 0 citations
Drug-drug interactions (DDIs) are a major cause of adverse drug events in clinical practice, especially under polypharmacy settings where patients receive multiple medications simultaneously. Reliable computational prediction of DDIs is therefore essential for improving medication safety and supporting clinical decision-making. Despite recent advances in computational DDI prediction, existing methods often struggle to jointly model multi-granularity pharmacological semantics and stereochemical molecular characteristics, limiting their ability to generalize to previously unseen drugs under cold-start scenarios. To address these limitations, we propose DSMV-DDI, a multimodal representation learning framework for drug-drug interaction prediction that integrates biomedical knowledge graph topology, chemical substructure features, dual-level pharmacological semantic representations, and stereochemical molecular visual representations derived from three-dimensional molecular conformations. In particular, the proposed dual-level semantic strategy jointly characterizes interaction-level pharmacological associations and intrinsic single-drug functional properties, enabling complementary modeling of pharmacological information across different semantic granularities. Furthermore, molecular visual representation learning captures geometric and spatial characteristics beyond topology-based molecular representations, improving generalization to topologically unseen drugs. Extensive experiments on real-world DDI datasets demonstrate that DSMV-DDI outperforms state-of-the-art methods, achieving an accuracy of 0.967 and an AUPR of 0.992 under the conventional setting. The proposed framework also maintains strong performance under both partial and complete cold-start settings. Ablation analyses show that dual-level pharmacological semantics contribute most to overall performance, while molecular visual representations provide complementary geometric information that further improves prediction accuracy.
Fangni Chen, Tingting Jiang, Shuai Yang et al.· Journal of Molecular Graphic...· 0 citations
Empirical evaluation and robustness experiments show that M2DDI maintains high predictive accuracy even when modality-specific information is partially missing, outperforming existing methods under similar conditions and establish M2DDI as an effective and mechanism-aware solution for comprehensive DDI prediction.
Runqing Xu, Siyi Liu, Haoyang Li et al.· Proceedings of the 32nd ACM...· 0 citations
SGTL-DDA is proposed, a novel graph transformer framework designed to incorporate structural information and domain-specific knowledge from heterogeneous biological information networks (HBINs) that successfully identifies both known therapeutics and novel repositioning candidates, supported by molecular docking results and literature evidence.
Bowei Zhao, Hui Zhao, Yu-an Huang et al.· IEEE transactions on computa...· 0 citations
GoMA-DTA is proposed, a framework integrating gene ontology (GO) functional annotations with protein semantic features with channelwise gating mechanism that uses functional semantics as anchors to dynamically recalibrate ESM-2embeddings, achieving adaptive semantic filtering.
An Xiong, Zheyu Zhou, Yazi Li et al.· IEEE Transactions on Neural...· 0 citations