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Drug-Related Side Effects and Adverse Reactions

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A Novel Deep Learning Model for Drug-drug Interactions.

Current computer-aided drug design
INTRODUCTION: Drug-drug interactions (DDIs) can lead to adverse events and compromised treatment efficacy that emphasize the need for accurate prediction and understanding of these interactions.

Use of artificial intelligence chatbots in clinical management of immune-related adverse events.

Journal for immunotherapy of cancer
BACKGROUND: Artificial intelligence (AI) chatbots have become a major source of general and medical information, though their accuracy and completeness are still being assessed. Their utility to answer questions surrounding immune-related adverse eve...

Quantum-to-Classical Neural Network Transfer Learning Applied to Drug Toxicity Prediction.

Journal of chemical theory and computation
Toxicity is a roadblock that prevents an inordinate number of drugs from being used in potentially life-saving applications. Deep learning provides a promising solution to finding ideal drug candidates; however, the vastness of chemical space coupled...

MGDDI: A multi-scale graph neural networks for drug-drug interaction prediction.

Methods (San Diego, Calif.)
Drug-drug interaction (DDI) prediction is crucial for identifying interactions within drug combinations, especially adverse effects due to physicochemical incompatibility. While current methods have made strides in predicting adverse drug interaction...

Optimizing drug therapy for older adults: shifting away from problematic polypharmacy.

Expert opinion on pharmacotherapy
INTRODUCTION: The accelerated discovery and production of pharmaceutical products has resulted in many positive outcomes. However, this progress has also contributed to problematic polypharmacy, one of the rapidly growing threats to public health in ...

Accurate prediction of drug combination risk levels based on relational graph convolutional network and multi-head attention.

Journal of translational medicine
BACKGROUND: Accurately identifying the risk level of drug combinations is of great significance in investigating the mechanisms of combination medication and adverse reactions. Most existing methods can only predict whether there is an interaction be...

Prediction of adverse drug reactions due to genetic predisposition using deep neural networks.

Molecular informatics
Drug development is a long and costly process, often limited by the toxicity and adverse drug reactions (ADRs) caused by drug candidates. Even on the market, some drugs can cause strong ADRs that can vary depending on an individual polymorphism. The ...

FetoML: Interpretable predictions of the fetotoxicity of drugs based on machine learning approaches.

Molecular informatics
Pregnant females may use medications to manage health problems that develop during pregnancy or that they had prior to pregnancy. However, using medications during pregnancy has a potential risk to the fetus. Assessing the fetotoxicity of drugs is es...

Trust but Verify: Lessons Learned for the Application of AI to Case-Based Clinical Decision-Making From Postmarketing Drug Safety Assessment at the US Food and Drug Administration.

Journal of medical Internet research
Adverse drug reactions are a common cause of morbidity in health care. The US Food and Drug Administration (FDA) evaluates individual case safety reports of adverse events (AEs) after submission to the FDA Adverse Event Reporting System as part of it...

Advancing medical imaging: detecting polypharmacy and adverse drug effects with Graph Convolutional Networks (GCN).

BMC medical imaging
Polypharmacy involves an individual using many medications at the same time and is a frequent healthcare technique used to treat complex medical disorders. Nevertheless, it also presents substantial risks of negative medication responses and interact...