AIMC Topic: Drug Interactions

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Comparing the applications of machine learning, PBPK, and population pharmacokinetic models in pharmacokinetic drug-drug interaction prediction.

CPT: pharmacometrics & systems pharmacology
The gold-standard approach for modeling pharmacokinetic mediated drug-drug interactions is the use of physiologically-based pharmacokinetic modeling and population pharmacokinetics. However, these models require extensive amounts of drug-specific dat...

Prediction of drug-drug interaction events using graph neural networks based feature extraction.

Scientific reports
The prevalence of multi_drug therapies has been increasing in recent years, particularly among the elderly who are suffering from several diseases. However, unexpected Drug_Drug interaction (DDI) can cause adverse reactions or critical toxicity, whic...

GCHN-DTI: Predicting drug-target interactions by graph convolution on heterogeneous networks.

Methods (San Diego, Calif.)
Determining the interaction of drug and target plays a key role in the process of drug development and discovery. The calculation methods can predict new interactions and speed up the process of drug development. In recent studies, the network-based ...

Multitype drug interaction prediction based on the deep fusion of drug features and topological relationships.

PloS one
Drug-drug interaction (DDI) prediction has received considerable attention from industry and academia. Most existing methods predict DDIs from drug attributes or relationships with neighbors, which does not guarantee that informative drug embeddings ...

IMSE: interaction information attention and molecular structure based drug drug interaction extraction.

BMC bioinformatics
BACKGROUND: Extraction of drug drug interactions from biomedical literature and other textual data is an important component to monitor drug-safety and this has attracted attention of many researchers in healthcare. Existing works are more pivoted ar...

A Comprehensive Review of Computational Methods For Drug-Drug Interaction Detection.

IEEE/ACM transactions on computational biology and bioinformatics
The detection of drug-drug interactions (DDIs) is a crucial task for drug safety surveillance, which provides effective and safe co-prescriptions of multiple drugs. Since laboratory researches are often complicated, costly and time-consuming, it's ur...

Matrix factorization with denoising autoencoders for prediction of drug-target interactions.

Molecular diversity
Drug-target interaction is crucial in the discovery of new drugs. Computational methods can be used to identify new drug-target interactions at low costs and with reasonable accuracy. Recent studies pay more attention to machine-learning methods, ran...

Does adding the drug-drug similarity to drug-target interaction prediction methods make a noticeable improvement in their efficiency?

BMC bioinformatics
Predicting drug-target interactions (DTIs) has become an important bioinformatics issue because it is one of the critical and preliminary stages of drug repositioning. Therefore, scientists are trying to develop more accurate computational methods fo...

A Drug Recommendation Model Based on Message Propagation and DDI Gating Mechanism.

IEEE journal of biomedical and health informatics
Drug recommendation task based on the deep learning model has been widely studied and applied in the health care field in recent years. However, the accuracy of drug recommendation models still needs to be improved. In addition, the existing recommen...