Benchmarking Graph Learning for Drug-Drug Interaction Prediction

• URL: http://arxiv.org/abs/2410.18583v2
• Date: Tue, 29 Oct 2024 06:07:49 GMT
• Title: Benchmarking Graph Learning for Drug-Drug Interaction Prediction
• Authors: Zhenqian Shen, Mingyang Zhou, Yongqi Zhang, Quanming Yao,
• Abstract summary: Predicting drug-drug interaction (DDI) plays an important role in pharmacology and healthcare. Recent graph learning methods have been introduced to predict drug-drug interactions. We propose a DDI prediction benchmark on graph learning.
• Score: 30.712106722531313
• License:
• Abstract: Predicting drug-drug interaction (DDI) plays an important role in pharmacology and healthcare for identifying potential adverse interactions and beneficial combination therapies between drug pairs. Recently, a flurry of graph learning methods have been introduced to predict drug-drug interactions. However, evaluating existing methods has several limitations, such as the absence of a unified comparison framework for DDI prediction methods, lack of assessments in meaningful real-world scenarios, and insufficient exploration of side information usage. In order to address these unresolved limitations in the literature, we propose a DDI prediction benchmark on graph learning. We first conduct unified evaluation comparison among existing methods. To meet realistic scenarios, we further evaluate the performance of different methods in settings with new drugs involved and examine the performance across different DDI types. Component analysis is conducted on the biomedical network to better utilize side information. Through this work, we hope to provide more insights for the problem of DDI prediction. Our implementation and data is open-sourced at https://anonymous.4open.science/r/DDI-Benchmark-ACD9/.

Related papers

• RGDA-DDI: Residual graph attention network and dual-attention based framework for drug-drug interaction prediction [4.044376666671973]
  We propose RGDA-DDI, a residual graph attention network (residual-GAT) and dual-attention based framework for drug-drug interaction prediction. A series of evaluation metrics demonstrate that the RGDA-DDI significantly improved DDI prediction performance on two public benchmark datasets.
  arXiv  Detail & Related papers  (2024-08-27T17:13:56Z)
• 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)
• Learning to Describe for Predicting Zero-shot Drug-Drug Interactions [54.172575323610175]
  Adverse drug-drug interactions can compromise the effectiveness of concurrent drug administration. Traditional computational methods for DDI prediction may fail to capture interactions for new drugs due to the lack of knowledge. We propose TextDDI with a language model-based DDI predictor and a reinforcement learning(RL)-based information selector.
  arXiv  Detail & Related papers  (2024-03-13T09:42:46Z)
• Accurate and interpretable drug-drug interaction prediction enabled by knowledge subgraph learning [39.66471292348325]
  We present KnowDDI, a graph neural network-based method that addresses the challenge of discovering potential drug-drug interactions. KnowDDI enhances drug representations by adaptively leveraging rich neighborhood information from large biomedical knowledge graphs. As an original open-source tool, KnowDDI can help detect possible interactions in a broad range of relevant interaction prediction tasks.
  arXiv  Detail & Related papers  (2023-11-25T15:44:28Z)
• ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction [49.56476929112382]
  Adverse drug reaction (ADR) prediction plays a crucial role in both health care and drug discovery. We propose ADRNet, a generalized collaborative filtering framework combining clinical and non-clinical data for drug-ADR prediction.
  arXiv  Detail & Related papers  (2023-08-03T11:28:12Z)
• 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)
• DDI Prediction via Heterogeneous Graph Attention Networks [0.0]
  Polypharmacy is the use of multiple drugs together. Drug-drug interaction (DDI) is the activity that occurs when the impact of one drug changes when combined with another. We present a novel heterogeneous graph attention model, HAN-DDI, to predict drug-drug interactions.
  arXiv  Detail & Related papers  (2022-07-12T16:59:06Z)
• 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)
• AttentionDDI: Siamese Attention-based Deep Learning method for drug-drug interaction predictions [0.9176056742068811]
  Drug-drug interactions (DDIs) refer to processes triggered by the administration of two or more drugs leading to side effects beyond those observed when drugs are administered by themselves. Due to the massive number of possible drug pairs, it is nearly impossible to experimentally test all combinations and discover previously unobserved side effects. We propose a Siamese self-attention multi-modal neural network for DDI prediction that integrates multiple drug similarity measures.
  arXiv  Detail & Related papers  (2020-12-24T13:33:07Z)

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.