SafeDrug: Dual Molecular Graph Encoders for Safe Drug Recommendations

• URL: http://arxiv.org/abs/2105.02711v1
• Date: Wed, 5 May 2021 00:20:48 GMT
• Title: SafeDrug: Dual Molecular Graph Encoders for Safe Drug Recommendations
• Authors: Chaoqi Yang, Cao Xiao, Fenglong Ma, Lucas Glass, Jimeng Sun
• Abstract summary: 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.
• Score: 59.590084937600764
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medication recommendation is an essential task of AI for healthcare. Existing works focused on recommending drug combinations for patients with complex health conditions solely based on their electronic health records. Thus, they have the following limitations: (1) some important data such as drug molecule structures have not been utilized in the recommendation process. (2) drug-drug interactions (DDI) are modeled implicitly, which can lead to sub-optimal results. To address these limitations, we propose a DDI-controllable drug recommendation model named SafeDrug to leverage drugs' molecule structures and model DDIs explicitly. SafeDrug is equipped with a global message passing neural network (MPNN) module and a local bipartite learning module to fully encode the connectivity and functionality of drug molecules. SafeDrug also has a controllable loss function to control DDI levels in the recommended drug combinations effectively. 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. Moreover, SafeDrug also requires much fewer parameters than previous deep learning-based approaches, leading to faster training by about 14% and around 2x speed-up in inference.

Related papers

• 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)
• 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)
• Knowledge-Driven New Drug Recommendation [88.35607943144261]
  We develop a drug-dependent multi-phenotype few-shot learner to bridge the gap between existing and new drugs. EDGE eliminates the false-negative supervision signal using an external drug-disease knowledge base. Results show that EDGE achieves 7.3% improvement on the ROC-AUC score over the best baseline.
  arXiv  Detail & Related papers  (2022-10-11T16:07:52Z)
• 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)
• 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.