AIMC Topic: Drug-Related Side Effects and Adverse Reactions

Clear Filters Showing 81 to 90 of 343 articles

Review of machine learning and deep learning models for toxicity prediction.

Experimental biology and medicine (Maywood, N.J.)
The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional ...

Advanced deep learning techniques for early disease prediction in cauliflower plants.

Scientific reports
Agriculture plays a pivotal role in the economies of developing countries by providing livelihoods, sustenance, and employment opportunities in rural areas. However, crop diseases pose a significant threat to both farmers' incomes and food security. ...

DFRscore: Deep Learning-Based Scoring of Synthetic Complexity with Drug-Focused Retrosynthetic Analysis for High-Throughput Virtual Screening.

Journal of chemical information and modeling
Recently emerging generative AI models enable us to produce a vast number of compounds for potential applications. While they can provide novel molecular structures, the synthetic feasibility of the generated molecules is often questioned. To address...

Identifying the serious clinical outcomes of adverse reactions to drugs by a multi-task deep learning framework.

Communications biology
Adverse Drug Reactions (ADRs) have a direct impact on human health. As continuous pharmacovigilance and drug monitoring prove to be costly and time-consuming, computational methods have emerged as promising alternatives. However, most existing comput...

Circulating Biomarkers Instead of Genotyping to Establish Metabolizer Phenotypes.

Annual review of pharmacology and toxicology
Pharmacogenomics (PGx) enables personalized treatment for the prediction of drug response and to avoid adverse drug reactions. Currently, PGx mainly relies on the genetic information of absorption, distribution, metabolism, and excretion (ADME) targe...

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...

Will the future of pharmacovigilance be more automated?

Expert opinion on drug safety
INTRODUCTION: Artificial intelligence (AI) based tools offer new opportunities for pharmacovigilance (PV) activities. Nevertheless, their contribution to PV needs to be tailored to preserve and strengthen medical and pharmacological expertise in drug...

Automatic Extraction of Comprehensive Drug Safety Information from Adverse Drug Event Narratives in the Korea Adverse Event Reporting System Using Natural Language Processing Techniques.

Drug safety
INTRODUCTION: Concerns have been raised over the quality of drug safety information, particularly data completeness, collected through spontaneous reporting systems (SRS), although regulatory agencies routinely use SRS data to guide their pharmacovig...