AIMC Topic: Drug Interactions

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Predicting Antioxidant Synergism via Artificial Intelligence and Benchtop Data.

Journal of agricultural and food chemistry
Lipid oxidation is a major issue affecting products containing unsaturated fatty acids as ingredients or components, leading to the formation of low molecular weight species with diverse functional groups that impart off-odors and off-flavors. Aiming...

MM-GANN-DDI: Multimodal Graph-Agnostic Neural Networks for Predicting Drug-Drug Interaction Events.

Computers in biology and medicine
Personalized treatment of complex diseases relies on combined medication. However, the occurrence of unexpected drug-drug interactions (DDIs) in these combinations can lead to adverse effects or even fatalities. Although recent computational methods ...

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies.

Chemical research in toxicology
Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches hav...

Application of Artificial Intelligence in Drug-Drug Interactions Prediction: A Review.

Journal of chemical information and modeling
Drug-drug interactions (DDI) are a critical aspect of drug research that can have adverse effects on patients and can lead to serious consequences. Predicting these events accurately can significantly improve clinicians' ability to make better decisi...

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...