AIMC Topic: Drug-Related Side Effects and Adverse Reactions
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Accurate prediction of drug combination risk levels based on relational graph convolutional network and multi-head attention.
Journal of translational medicine
Jun 16, 2024
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
Jun 8, 2024
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
Jun 8, 2024
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
Jun 6, 2024
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...
Use of artificial intelligence chatbots in clinical management of immune-related adverse events.
Journal for immunotherapy of cancer
May 30, 2024
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
May 25, 2024
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.)
May 15, 2024
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...
Learning with an evolving medicine label: how artificial intelligence-based medication recommendation systems must adapt to changing medication labels.
Expert opinion on drug safety
May 6, 2024
INTRODUCTION: Artificial intelligence or machine learning (AI/ML) based systems can help personalize prescribing decisions for individual patients. The recommendations of these clinical decision support systems must relate to the "label" of the medic...
A Pilot, Predictive Surveillance Model in Pharmacovigilance Using Machine Learning Approaches.
Advances in therapy
May 5, 2024
INTRODUCTION: The identification of a new adverse event (AE) caused by a drug product is one of the key activities in the pharmaceutical industry to ensure the safety profile of a drug product. Machine learning (ML) has the potential to assist with s...
A deep learning based multi-model approach for predicting drug-like chemical compound's toxicity.
Methods (San Diego, Calif.)
May 1, 2024
Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate predic...
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