Aug 2026· Journal of Chemical Information and Modeling· 0 citations· 46 references
TL;DR
Experimental results on multiple benchmark data sets demonstrate that MMU-DPI outperforms several state-of-the-art DPI prediction methods and indicate that MMU-DPI can serve as a useful computational tool for drug discovery.
Abstract
Accurate prediction of drug–protein interactions (DPIs) is crucial for accelerating the drug discovery process. However, the scarcity of experimentally validated interactions can limit the learning of transferable interaction patterns, particularly for previously unseen drugs and proteins. To address this fundamental challenge, we propose the MMU-DPI framework. A key component of this framework is a Label Mix strategy tailored to multimodal DPI prediction, which performs interpolation only in the label space while keeping the input modalities unchanged. This strategy provides stochastic soft-target regularization and improves generalization performance under reduced-data and independent external Cold-both evaluation settings. To effectively process and utilize multimodal data, MMU-DPI adopts a multimodal dual-branch architecture. The first branch uses a Message Passing Neural Network (MPNN) to extract structured representations from drug molecular graphs. It also uses a Convolutional Neural Network (CNN) to capture key biological and functional features from amino acid sequences. The second branch constructs a heterogeneous interaction graph and uses a Graph Attention Network (GAT) to learn deep contextual relationships between drugs and proteins. A learnable global fusion weight combines complementary branch logits to generate the final prediction for each drug–protein pair. Experimental results on multiple benchmark data sets demonstrate that MMU-DPI outperforms several state-of-the-art DPI prediction methods. Case studies further support the ability of MMU-DPI to identify potential DPIs. These results indicate that MMU-DPI can serve as a useful computational tool for drug discovery.
This paper proposes TextDTI, a multimodal framework that simultaneously exploits sequential and structural representations and enhances feature alignment through adversarial learning and contrastive loss, resulting in robust and high-performance DTI prediction.
Jiaqi Deng, Senyu Tang, Jijun Tang et al.· Journal of Chemical Informat...· 0 citations
This work investigated case studies for interactions with bupropion and ritonavir with integrated gradients and identified molecular regions associated with known CYP-mediated interaction mechanisms.
Andrew Disharoon, Shifi Pasupuleti, Clark Thurston et al.· Frontiers in Drug Safety and...· 0 citations
Prediction of Drug Target Affinity (DTA) is essential for accelerating computational drug discovery and reducing experimental costs. However, traditional experimental approaches for DTA estimation are resource-intensive and are further challenged by the structural flexibility of both drugs and target proteins. In this work, we propose the PCBERT-GAT-DFFNN-DTA model, a three-stage deep cross-modal representation fusion framework for accurate DTA prediction. In the first stage, variable-length protein sequences are transformed into contextual representations using ProtBERT to obtain fixed-size protein embeddings. Drug molecules are represented using two modalities: sequence-based embeddings generated from ChemBERT and structure-based embeddings learned from molecular graphs using a Graph Attention Network (GAT). In the second stage, each modality is processed through dedicated subnetworks to refine features and reduce dimensionality while preserving modality-specific information. In the final stage, the refined representations are fused and passed to a Deep Feed-Forward Neural Network (DFFNN) to predict drug target binding affinity. The proposed model consistently outperformed most baseline methods under the S1-S3 evaluation settings across the benchmark datasets. Under the more challenging S4 blind setting, the model achieved strong performance on the KIBA dataset and competitive results on the Davis and Metz datasets. Compared with LLMDTA, the proposed approach achieves significant improvements in R2 scores across all datasets, demonstrating its effectiveness in learning complex drug protein interactions for reliable DTA prediction.
Essmily Simon, Sanjay S. Bankapur· Analytical Biochemistry· 0 citations
This work proposes GraphTransDTI, a synergistic hybrid framework that integrates a Graph Transformer to represent drug graph structures, a CNN-BiLSTM network to encode protein sequence context, and a Cross-Attention mechanism to model cross-domain interactions.
Vang V. Le, Mai Thi Anh Nhu, Pham Truong Viet Thong· PLoS ONE· 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
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