DDI Prediction via Heterogeneous Graph Attention Networks
- URL: http://arxiv.org/abs/2207.05672v1
- Date: Tue, 12 Jul 2022 16:59:06 GMT
- Title: DDI Prediction via Heterogeneous Graph Attention Networks
- Authors: Farhan Tanvir, Khaled Mohammed Saifuddin, Esra Akbas
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Polypharmacy, defined as the use of multiple drugs together, is a standard
treatment method, especially for severe and chronic diseases. However, using
multiple drugs together may cause interactions between drugs. Drug-drug
interaction (DDI) is the activity that occurs when the impact of one drug
changes when combined with another. DDIs may obstruct, increase, or decrease
the intended effect of either drug or, in the worst-case scenario, create
adverse side effects. While it is critical to detect DDIs on time, it is
timeconsuming and expensive to identify them in clinical trials due to their
short duration and many possible drug pairs to be considered for testing. As a
result, computational methods are needed for predicting DDIs. In this paper, we
present a novel heterogeneous graph attention model, HAN-DDI to predict
drug-drug interactions. We create a heterogeneous network of drugs with
different biological entities. Then, we develop a heterogeneous graph attention
network to learn DDIs using relations of drugs with other entities. It consists
of an attention-based heterogeneous graph node encoder for obtaining drug node
representations and a decoder for predicting drug-drug interactions. Further,
we utilize comprehensive experiments to evaluate of our model and to compare it
with state-of-the-art models. Experimental results show that our proposed
method, HAN-DDI, outperforms the baselines significantly and accurately
predicts DDIs, even for new drugs.
Related papers
- Benchmarking Graph Learning for Drug-Drug Interaction Prediction [30.712106722531313]
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.
arXiv Detail & Related papers (2024-10-24T09:35:34Z) - 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) - Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural
Network with Biomedical Network [69.16939798838159]
We propose EmerGNN, a graph neural network (GNN) that can effectively predict interactions for emerging drugs.
EmerGNN learns pairwise representations of drugs by extracting the paths between drug pairs, propagating information from one drug to the other, and incorporating the relevant biomedical concepts on the paths.
Overall, EmerGNN has higher accuracy than existing approaches in predicting interactions for emerging drugs and can identify the most relevant information on the biomedical network.
arXiv Detail & Related papers (2023-11-15T06:34:00Z) - 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) - HyGNN: Drug-Drug Interaction Prediction via Hypergraph Neural Network [0.0]
Drug-Drug Interactions (DDIs) may hamper the functionalities of drugs, and in the worst scenario, they may lead to adverse drug reactions (ADRs)
This paper proposes a novel Hypergraph Neural Network (HyGNN) model based on only the SMILES string of drugs, available for any drug, for the DDI prediction problem.
Our proposed HyGNN model effectively predicts DDIs and impressively outperforms the baselines with a maximum ROC-AUC and PR-AUC of 97.9% and 98.1%, respectively.
arXiv Detail & Related papers (2022-06-25T22:48:27Z) - 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) - SafeDrug: Dual Molecular Graph Encoders for Safe Drug Recommendations [59.590084937600764]
We propose a DDI-controllable drug recommendation model named SafeDrug to leverage drugs' molecule structures and model DDIs explicitly.
On a benchmark dataset, our SafeDrug is relatively shown to reduce DDI by 19.43% and improves 2.88% on Jaccard similarity between recommended and actually prescribed drug combinations over previous approaches.
arXiv Detail & Related papers (2021-05-05T00:20:48Z) - 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) - Two Step Joint Model for Drug Drug Interaction Extraction [82.49278654043577]
Drug-Drug Interaction (DDI) Extraction from Drug Labels challenge of Text Analysis Conference (TAC) 2018.
We propose a two step joint model to detect DDI and it's related mentions jointly.
A sequence tagging system (CNN-GRU encoder-decoder) finds precipitants first and search its fine-grained Trigger and determine the DDI for each precipitant in the second step.
arXiv Detail & Related papers (2020-08-28T15:30:08Z)
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.