AttentionDDI: Siamese Attention-based Deep Learning method for drug-drug interaction predictions

• URL: http://arxiv.org/abs/2012.13248v1
• Date: Thu, 24 Dec 2020 13:33:07 GMT
• Title: AttentionDDI: Siamese Attention-based Deep Learning method for drug-drug interaction predictions
• Authors: Kyriakos Schwarz, Ahmed Allam, Nicolas Andres Perez Gonzalez, Michael Krauthammer
• Abstract summary: 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.
• Score: 0.9176056742068811
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background: 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. Therefore, machine learning based methods are being used to address this issue. Methods: We propose a Siamese self-attention multi-modal neural network for DDI prediction that integrates multiple drug similarity measures that have been derived from a comparison of drug characteristics including drug targets, pathways and gene expression profiles. Results: Our proposed DDI prediction model provides multiple advantages: 1) It is trained end-to-end, overcoming limitations of models composed of multiple separate steps, 2) it offers model explainability via an Attention mechanism for identifying salient input features and 3) it achieves similar or better prediction performance (AUPR scores ranging from 0.77 to 0.92) compared to state-of-the-art DDI models when tested on various benchmark datasets. Novel DDI predictions are further validated using independent data resources. Conclusions: We find that a Siamese multi-modal neural network is able to accurately predict DDIs and that an Attention mechanism, typically used in the Natural Language Processing domain, can be beneficially applied to aid in DDI model explainability.

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)
• ExDDI: Explaining Drug-Drug Interaction Predictions with Natural Language [19.426453222204714]
  We propose to generate natural language explanations for DDI predictions. We have collected DDI explanations from DDInter and DrugBank. Our models can provide accurate explanations for unknown DDIs between known drugs.
  arXiv  Detail & Related papers  (2024-09-09T13:23:14Z)
• 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)
• Physical formula enhanced multi-task learning for pharmacokinetics prediction [54.13787789006417]
  A major challenge for AI-driven drug discovery is the scarcity of high-quality data. We develop a formula enhanced mul-ti-task learning (PEMAL) method that predicts four key parameters of pharmacokinetics simultaneously. Our experiments reveal that PEMAL significantly lowers the data demand, compared to typical Graph Neural Networks.
  arXiv  Detail & Related papers  (2024-04-16T07:42:55Z)
• 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)
• 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)
• Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response [49.86828302591469]
  In this paper, we apply transfer learning to the prediction of anti-cancer drug response. We apply the classic transfer learning framework that trains a prediction model on the source dataset and refines it on the target dataset. The ensemble transfer learning pipeline is implemented using LightGBM and two deep neural network (DNN) models with different architectures.
  arXiv  Detail & Related papers  (2020-05-13T20:29:48Z)

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.