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Drug Interactions

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DEDTI versus IEDTI: efficient and predictive models of drug-target interactions.

Scientific reports
Drug repurposing is an active area of research that aims to decrease the cost and time of drug development. Most of those efforts are primarily concerned with the prediction of drug-target interactions. Many evaluation models, from matrix factorizati...

Multiple sampling schemes and deep learning improve active learning performance in drug-drug interaction information retrieval analysis from the literature.

Journal of biomedical semantics
BACKGROUND: Drug-drug interaction (DDI) information retrieval (IR) is an important natural language process (NLP) task from the PubMed literature. For the first time, active learning (AL) is studied in DDI IR analysis. DDI IR analysis from PubMed abs...

DrugormerDTI: Drug Graphormer for drug-target interaction prediction.

Computers in biology and medicine
Drug-target interactions (DTI) prediction is a crucial task in drug discovery. Existing computational methods accelerate the drug discovery in this respect. However, most of them suffer from low feature representation ability, significantly affecting...

Enhancing Drug-Drug Interaction Prediction Using Deep Attention Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Drug-drug interactions are one of the main concerns in drug discovery. Accurate prediction of drug-drug interactions plays a key role in increasing the efficiency of drug research and safety when multiple drugs are co-prescribed. With various data so...

CNN-Siam: multimodal siamese CNN-based deep learning approach for drug‒drug interaction prediction.

BMC bioinformatics
BACKGROUND: Drug‒drug interactions (DDIs) are reactions between two or more drugs, i.e., possible situations that occur when two or more drugs are used simultaneously. DDIs act as an important link in both drug development and clinical treatment. Sin...

Developing a Knowledge Graph for Pharmacokinetic Natural Product-Drug Interactions.

Journal of biomedical informatics
BACKGROUND: Pharmacokinetic natural product-drug interactions (NPDIs) occur when botanical or other natural products are co-consumed with pharmaceutical drugs. With the growing use of natural products, the risk for potential NPDIs and consequent adve...

The curse and blessing of abundance-the evolution of drug interaction databases and their impact on drug network analysis.

GigaScience
BACKGROUND: Widespread bioinformatics applications such as drug repositioning or drug-drug interaction prediction rely on the recent advances in machine learning, complex network science, and comprehensive drug datasets comprising the latest research...

MSEDDI: Multi-Scale Embedding for Predicting Drug-Drug Interaction Events.

International journal of molecular sciences
A norm in modern medicine is to prescribe polypharmacy to treat disease. The core concern with the co-administration of drugs is that it may produce adverse drug-drug interaction (DDI), which can cause unexpected bodily injury. Therefore, it is essen...

MecDDI: Clarified Drug-Drug Interaction Mechanism Facilitating Rational Drug Use and Potential Drug-Drug Interaction Prediction.

Journal of chemical information and modeling
Drug-drug interactions (DDIs) are a major concern in clinical practice and have been recognized as one of the key threats to public health. To address such a critical threat, many studies have been conducted to clarify the mechanism underlying each D...

DRaW: prediction of COVID-19 antivirals by deep learning-an objection on using matrix factorization.

BMC bioinformatics
BACKGROUND: Due to the high resource consumption of introducing a new drug, drug repurposing plays an essential role in drug discovery. To do this, researchers examine the current drug-target interaction (DTI) to predict new interactions for the appr...