DPASyn: Mechanism-Aware Drug Synergy Prediction via Dual Attention and Precision-Aware Quantization

• URL: http://arxiv.org/abs/2505.19144v2
• Date: Sat, 20 Sep 2025 13:45:20 GMT
• Title: DPASyn: Mechanism-Aware Drug Synergy Prediction via Dual Attention and Precision-Aware Quantization
• Authors: Yuxuan Nie, Yutong Song, Jinjie Yang, Yupeng Song, Yujue Zhou, Hong Peng,
• Abstract summary: Drug combinations are essential in cancer therapy, leveraging synergistic drug-drug interactions (DDI) to enhance efficacy and combat resistance.<n>We propose DPASyn, a novel drug synergy prediction framework featuring a dual-attention mechanism and Precision-Aware Quantization (PAQ)<n>The dual-attention architecture jointly models intra-drug structures and inter-drug interactions via shared projections and cross-drug attention, enabling fine-grained, biologically plausible synergy modeling.
• Score: 2.8158058913878268
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Drug combinations are essential in cancer therapy, leveraging synergistic drug-drug interactions (DDI) to enhance efficacy and combat resistance. However, the vast combinatorial space makes experimental screening impractical, and existing computational models struggle to capture the complex, bidirectional nature of DDIs, often relying on independent drug encoding or simplistic fusion strategies that miss fine-grained inter-molecular dynamics. Moreover, state-of-the-art graph-based approaches suffer from high computational costs, limiting scalability for real-world drug discovery. To address this, we propose DPASyn, a novel drug synergy prediction framework featuring a dual-attention mechanism and Precision-Aware Quantization (PAQ). The dual-attention architecture jointly models intra-drug structures and inter-drug interactions via shared projections and cross-drug attention, enabling fine-grained, biologically plausible synergy modeling. While this enhanced expressiveness brings increased computational resource consumption, our proposed PAQ strategy complements it by dynamically optimizing numerical precision during training based on feature sensitivity-reducing memory usage by 40% and accelerating training threefold without sacrificing accuracy. With LayerNorm-stabilized residual connections for training stability, DPASyn outperforms seven state-of-the-art methods on the O'Neil dataset (13,243 combinations) and supports full-batch processing of up to 256 graphs on a single GPU, setting a new standard for efficient and expressive drug synergy prediction.

Related papers

• Tensor-DTI: Enhancing Biomolecular Interaction Prediction with Contrastive Embedding Learning [0.015229507502478598]
  We propose a contrastive learning framework that integrates multimodal embeddings from molecular graphs, protein language models, and binding-site predictions to improve interaction modeling.<n>Our findings highlight the benefits of integrating multimodal information with contrastive objectives to enhance interaction-prediction accuracy and to provide more interpretable and reliability-aware models for virtual screening splits.
  arXiv  Detail & Related papers  (2026-01-09T13:39:49Z)
• MolBridge: Atom-Level Joint Graph Refinement for Robust Drug-Drug Interaction Event Prediction [10.392084347375963]
  Drug combinations offer therapeutic benefits but also carry the risk of adverse drug-drug interactions (DDIs)<n>This work contributes to Web Mining and Content Analysis by developing graph-based methods for mining and analyzing drug-drug interaction networks.
  arXiv  Detail & Related papers  (2025-10-23T11:33:16Z)
• Bidirectional Representations Augmented Autoregressive Biological Sequence Generation:Application in De Novo Peptide Sequencing [51.12821379640881]
  Autoregressive (AR) models offer holistic, bidirectional representations but face challenges with generative coherence and scalability.<n>We propose a hybrid framework enhancing AR generation by dynamically integrating rich contextual information from non-autoregressive mechanisms.<n>A novel cross-decoder attention module enables the AR decoder to iteratively query and integrate these bidirectional features.
  arXiv  Detail & Related papers  (2025-10-09T12:52:55Z)
• A Hybrid Computational Intelligence Framework with Metaheuristic Optimization for Drug-Drug Interaction Prediction [0.8602553195689512]
  Drug-drug interactions (DDIs) are a leading cause of preventable adverse events, often complicating treatment and increasing healthcare costs.<n>We propose an interpretable and efficient framework that blends modern machine learning with domain knowledge to improve DDI prediction.<n>Our approach combines two complementary embeddings - Mol2Vec, which captures fragment-level structural patterns, and SMILES-BERT, which learns contextual chemical features.
  arXiv  Detail & Related papers  (2025-10-08T09:55:18Z)
• DrugImproverGPT: A Large Language Model for Drug Optimization with Fine-Tuning via Structured Policy Optimization [53.27954325490941]
  Finetuning a Large Language Model (LLM) is crucial for generating results towards specific objectives.<n>This research introduces a novel reinforcement learning algorithm to finetune a drug optimization LLM-based generative model.
  arXiv  Detail & Related papers  (2025-02-11T04:00:21Z)
• ScaffoldGPT: A Scaffold-based GPT Model for Drug Optimization [3.240904428766923]
  ScaffoldGPT is a Generative Pretrained Transformer (GPT) designed for drug optimization based on molecular scaffolds.<n>Our work comprises three key components: (1) A three-stage drug optimization approach that integrates pretraining, finetuning, and decoding optimization.<n>We demonstrate via a comprehensive evaluation on COVID and cancer benchmarks that ScaffoldGPT outperforms the competing baselines in drug optimization benchmarks.
  arXiv  Detail & Related papers  (2025-02-09T10:36:33Z)
• MD-Syn: Synergistic drug combination prediction based on the multidimensional feature fusion method and attention mechanisms [0.0]
  Drug combination therapies have shown promising therapeutic efficacy in complex diseases and have demonstrated the potential to reduce drug resistance.<n>The huge number of possible drug combinations makes it difficult to screen them all in traditional experiments.<n>In this study, we proposed MD-Syn, a computational framework, which is based on the multidimensional feature fusion method and multi-head attention mechanisms.
  arXiv  Detail & Related papers  (2025-01-14T06:50:56Z)
• Barlow Twins Deep Neural Network for Advanced 1D Drug-Target Interaction Prediction [0.0]
  BarlowDTI uses the powerful Barlow Twins architecture for feature-extraction. It achieves state-of-the-art predictive performance against multiple established benchmarks. By comparing co-crystal structures, we find that BarlowDTI effectively exploits catalytically active and stabilising residues.
  arXiv  Detail & Related papers  (2024-07-31T14:06:18Z)
• A Cross-Field Fusion Strategy for Drug-Target Interaction Prediction [85.2792480737546]
  Existing methods fail to utilize global protein information during DTI prediction. Cross-field information fusion strategy is employed to acquire local and global protein information. Siamese drug-target interaction SiamDTI prediction method achieves higher accuracy levels than other state-of-the-art (SOTA) methods on novel drugs and targets.
  arXiv  Detail & Related papers  (2024-05-23T13:25:20Z)
• ALNSynergy: a graph convolutional network with multi-representation alignment for drug synergy prediction [8.316187397380244]
  Drug combination refers to the use of two or more drugs to treat a specific disease at the same time. In this work, we propose ALNSynergy, a graph convolutional network with multi-representation alignment for predicting drug synergy.
  arXiv  Detail & Related papers  (2023-11-27T15:34:14Z)
• CongFu: Conditional Graph Fusion for Drug Synergy Prediction [8.939263684319263]
  CongFu is a Conditional Graph Fusion Layer designed to predict drug synergy. It achieves state-of-the-art results on 11 out of 12 benchmark datasets. We propose an explainability strategy for elucidating the effect of drugs on genes.
  arXiv  Detail & Related papers  (2023-05-23T20:46:17Z)
• IPCC-TP: Utilizing Incremental Pearson Correlation Coefficient for Joint Multi-Agent Trajectory Prediction [73.25645602768158]
  IPCC-TP is a novel relevance-aware module based on Incremental Pearson Correlation Coefficient to improve multi-agent interaction modeling. Our module can be conveniently embedded into existing multi-agent prediction methods to extend original motion distribution decoders.
  arXiv  Detail & Related papers  (2023-03-01T15:16:56Z)
• Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning [82.93806087715507]
  Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. Deep learning models have emerged as an efficient way to discover synergistic combinations. Our framework achieves state-of-the-art results in comparison with other deep learning-based methods.
  arXiv  Detail & Related papers  (2023-01-14T15:07:43Z)
• SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction [127.43571146741984]
  Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery. wet experiments remain the most reliable method, but they are time-consuming and resource-intensive. Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue. We present the SSM-DTA framework, which incorporates three simple yet highly effective strategies.
  arXiv  Detail & Related papers  (2022-06-20T14:53:25Z)
• Multi-View Substructure Learning for Drug-Drug Interaction Prediction [69.34322811160912]
  We propose a novel multi- view drug substructure network for DDI prediction (MSN-DDI) MSN-DDI learns chemical substructures from both the representations of the single drug (intra-view) and the drug pair (inter-view) simultaneously and utilizes the substructures to update the drug representation iteratively. Comprehensive evaluations demonstrate that MSN-DDI has almost solved DDI prediction for existing drugs by achieving a relatively improved accuracy of 19.32% and an over 99% accuracy under the transductive setting.
  arXiv  Detail & Related papers  (2022-03-28T05:44:29Z)
• Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer [98.8319016075089]
  We propose a novel approach to model intermolecular information with a three-way Transformer-based architecture. Intermolecular Graph Transformer (IGT) outperforms state-of-the-art approaches by 9.1% and 20.5% over the second best for binding activity and binding pose prediction respectively. IGT exhibits promising drug screening ability against SARS-CoV-2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses.
  arXiv  Detail & Related papers  (2021-10-14T13:28:02Z)
• DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations [0.9854322576538699]
  computational screening has become an important way to prioritize drug combinations. DeepDDS was superior to competitive methods by more than 16% predictive precision.
  arXiv  Detail & Related papers  (2021-07-06T08:25:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.