Drug-Drug Interaction Prediction with Wasserstein Adversarial Autoencoder-based Knowledge Graph Embeddings

• URL: http://arxiv.org/abs/2004.07341v2
• Date: Fri, 16 Oct 2020 03:02:02 GMT
• Title: Drug-Drug Interaction Prediction with Wasserstein Adversarial Autoencoder-based Knowledge Graph Embeddings
• Authors: Yuanfei Dai, Chenhao Guo, Wenzhong Guo, Carsten Eickhoff
• Abstract summary: We propose a new knowledge graph embedding framework for drug-drug interactions. In our framework, the autoencoder is employed to generate high-quality negative samples. The discriminator learns the embeddings of drugs and interactions based on both positive and negative triplets.
• Score: 22.562175708415392
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Interaction between pharmacological agents can trigger unexpected adverse events. Capturing richer and more comprehensive information about drug-drug interactions (DDI) is one of the key tasks in public health and drug development. Recently, several knowledge graph embedding approaches have received increasing attention in the DDI domain due to their capability of projecting drugs and interactions into a low-dimensional feature space for predicting links and classifying triplets. However, existing methods only apply a uniformly random mode to construct negative samples. As a consequence, these samples are often too simplistic to train an effective model. In this paper, we propose a new knowledge graph embedding framework by introducing adversarial autoencoders (AAE) based on Wasserstein distances and Gumbel-Softmax relaxation for drug-drug interactions tasks. In our framework, the autoencoder is employed to generate high-quality negative samples and the hidden vector of the autoencoder is regarded as a plausible drug candidate. Afterwards, the discriminator learns the embeddings of drugs and interactions based on both positive and negative triplets. Meanwhile, in order to solve vanishing gradient problems on the discrete representation--an inherent flaw in traditional generative models--we utilize the Gumbel-Softmax relaxation and the Wasserstein distance to train the embedding model steadily. We empirically evaluate our method on two tasks, link prediction and DDI classification. The experimental results show that our framework can attain significant improvements and noticeably outperform competitive baselines.

Related papers

• Regressor-free Molecule Generation to Support Drug Response Prediction [83.25894107956735]
  Conditional generation based on the target IC50 score can obtain a more effective sampling space. Regressor-free guidance combines a diffusion model's score estimation with a regression controller model's gradient based on number labels.
  arXiv  Detail & Related papers  (2024-05-23T13:22:17Z)
• 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)
• Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems [70.84976977950075]
  Real-world multi-agent systems are often dynamic and continuous, where the agents co-evolve and undergo changes in their trajectories and interactions over time. We propose a novel model that captures the continuous interaction among agents using a Graph Neural Network (GNN) as the ODE function. The key innovation of our model is to learn time-dependent representations of treatments and incorporate them into the ODE function, enabling precise predictions of potential outcomes.
  arXiv  Detail & Related papers  (2024-02-29T23:07:07Z)
• DDIPrompt: Drug-Drug Interaction Event Prediction based on Graph Prompt Learning [15.69547371747469]
  DDIPrompt is an innovative solution inspired by the recent advancements in graph prompt learning. Our framework aims to address these issues by leveraging intrinsic the knowledge from pre-trained models. Extensive experiments on two benchmark datasets demonstrate DDIPrompt's SOTA performance.
  arXiv  Detail & Related papers  (2024-02-18T06:22:01Z)
• 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)
• PGraphDTA: Improving Drug Target Interaction Prediction using Protein Language Models and Contact Maps [4.590060921188914]
  Key aspect of drug discovery involves identifying novel drug-target (DT) interactions. Protein-ligand interactions exhibit a continuum of binding strengths, known as binding affinity. We propose novel enhancements to enhance their performance.
  arXiv  Detail & Related papers  (2023-10-06T05:00:25Z)
• 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)
• 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)
• 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)
• DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations [90.27736364704108]
  We present DrugOOD, a systematic OOD dataset curator and benchmark for AI-aided drug discovery. DrugOOD comes with an open-source Python package that fully automates benchmarking processes. We focus on one of the most crucial problems in AIDD: drug target binding affinity prediction.
  arXiv  Detail & Related papers  (2022-01-24T12:32:48Z)

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.