Aug 2026· Intelligent Data Analysis· 0 citations· 44 references
TL;DR
The DRL-DSP is proposed, a novel dual representation learning framework designed to enhance drug synergy prediction by integrating molecular-level features from SMILES sequences with graph-level relational information from reconstructed molecular networks.
Abstract
Predicting drug synergy is crucial for optimizing drug combination therapies. However, it remains a challenging task to extract and integrate complex relational information from multiple data dimensions. This study proposes the DRL-DSP, a novel dual representation learning framework designed to enhance drug synergy prediction by integrating molecular-level features from SMILES sequences with graph-level relational information from reconstructed molecular networks. Our approach employs a SMILES-based data augmentation strategy, where randomized SMILES sequences are generated to enrich sequence representation diversity and enable a more comprehensive exploration of drug characteristics. Additionally, similarities between drug pairs are computed based on SMILES sequences to capture molecular relationships. A network is constructed for graph-level feature aggregation by integrating similarity-based edge weights into the adjacency matrix as an affinity matrix and incorporating feature matrices that reflect molecular properties. This combined representation improves the model's capability to capture molecular and relational information simultaneously. By utilizing encoding-decoding techniques for structural information of SMILES and convolution functions for molecular network representations, DRL-DSP integrates these complementary data resources to enhance drug synergy prediction. Furthermore, experiments under various conditions are conducted to verify the performance of DRL-DSP. Our approach addresses the limitations of single-modality methods and establishes a new paradigm for drug synergy prediction by integrating molecular and relational representations into a more effective and accurate framework.
Drug combination therapy plays an increasingly important role in the clinical treatment of complex diseases, such as cancer, as rational drug combinations can enhance therapeutic efficacy and reduce toxic side effects. However, existing methods still exhibit limitations in the granularity of drug molecular representation, drug interaction modeling, and cell line context awareness, which restrict further improvements in predictive performance. To address these issues, we propose FragSyn, a deep graph learning framework for predicting synergistic drug combinations based on molecular fragmentations. FragSyn first decomposes drug molecules into chemically meaningful fragments according to breaks of retrosynthetically interesting chemical substructure rules and learns fragment-level molecular representations through a graph isomorphism network with edge features. It then captures nonlinear relationships between drug pairs from multiple perspectives while introducing a gating modulation mechanism conditioned on cell line features, enabling drug representations to adapt dynamically to the cell line context. Finally, multisource features are fused to perform binary classification of synergy versus antagonism. FragSyn achieves AUC, AUPR, and ACC of 0.944, 0.942, and 0.872, respectively, outperforming eight baseline models, and demonstrates optimal generalization performance in both leave-one-out cross-validation and external validation. Ablation studies and interpretability analyses further validate the rationality of FragSyn and its ability to identify key fragments. These results indicate that FragSyn, through the synergistic design of fragment-level representation and cellular context awareness, provides an effective and interpretable new approach to synergistic drug combination prediction.
Lifeng Shao, Jianqiang Sun, Hong-Zhan Ma et al.· Journal of Chemical Informat...· 0 citations
Abstract Motivation Accurate prediction of drug-target interactions (DTIs) is fundamental to drug discovery and mechanistic understanding. While deep learning has advanced computational DTI prediction, most existing methods rely primarily on molecular structural representations, including drug structures and protein sequences, while overlooking cellular phenotypes that reflect downstream biological effects. Cell Painting enables high-content morphological profiling that captures systems-level responses to chemical and genetic perturbations but remains underutilized in DTI modeling. Integrating molecular information with cellular phenotypes offers an opportunity to improve both predictive performance and biological interpretability. Results We propose a two-stage contrastive learning framework integrating drug structures, protein sequences, and Cell Painting morphological profiles into a unified embedding space. Stage 1 learns modality-specific representations independently from structure-based and image-based data; Stage 2 aligns these via multi-positive contrastive learning to bridge molecular structural information with cellular phenotypes. Cross-modal retrieval achieves median Recall@10 values of 0.77 (random split) and 0.33 (scaffold split), outperforming bilinear and random baselines. In external DTI prediction on the BIOSNAP dataset, our model achieves an AUC of 0.92 with image-based representations and 0.90 under structure-only settings, surpassing existing methods. Model interpretation via integrated gradients reveals pathway-specific morphological signatures associated with drug targets, providing biologically interpretable insights into drug mechanisms. Availability https://github.com/YJRubyLai/Unified-DTI
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
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
DeepGCL is presented, a novel multi-modal framework that leverages multi-view graph contrastive learning to capture latent representations of pocket-drug interactions and their underlying molecular determinants and underscores the effectiveness of multi-view learning paradigms in capturing the multifaceted nature of drug-target interactions.
Hongmei Wang, Shisen Sun, Mujin Li et al.· IEEE journal of biomedical a...· 0 citations
A novel multiview feature fusion-based graph representation model (MFF-GRM) for predicting DDI that integrates drug molecular graphs, SMILES sequences, DDI information networks, and drug biological features to learn drug features more comprehensively.
Mengyuan Jin, Dan Liu, E. Benfenati et al.· Applied intelligence (Boston...· 0 citations