[HTML][HTML] Application of artificial intelligence and machine learning in early detection of adverse drug reactions (ADRs) and drug-induced toxicity
S Yang, S Kar - Artificial Intelligence Chemistry, 2023 - Elsevier
Adverse drug reactions (ADRs) and drug-induced toxicity are major challenges in drug
discovery, threatening patient safety and dramatically increasing healthcare expenditures …
discovery, threatening patient safety and dramatically increasing healthcare expenditures …
Evaluation of QSAR models for tissue-specific predictive toxicology and risk assessment of military-relevant chemical exposures: A systematic review
JL Fisher, KT Williams, LJ Schneider… - Computational …, 2024 - Elsevier
The use of in silico modeling tools for predictive toxicology has potential to improve force
health protection in the military by hel** to efficiently evaluate the risk of adverse health …
health protection in the military by hel** to efficiently evaluate the risk of adverse health …
Statistical machine learning approaches to liver disease prediction
Medical diagnoses have important implications for improving patient care, research, and
policy. For a medical diagnosis, health professionals use different kinds of pathological …
policy. For a medical diagnosis, health professionals use different kinds of pathological …
CNN with machine learning approaches using ExtraTreesClassifier and MRMR feature selection techniques to detect liver diseases on cloud
Liver disease is a significant global burden on health, with about a few hundred million
people suffering from chronic liver disease (CLD), with approximately 2 million deaths each …
people suffering from chronic liver disease (CLD), with approximately 2 million deaths each …
An explainable supervised machine learning model for predicting respiratory toxicity of chemicals using optimal molecular descriptors
Respiratory toxicity is a serious public health concern caused by the adverse effects of drugs
or chemicals, so the pharmaceutical and chemical industries demand reliable and precise …
or chemicals, so the pharmaceutical and chemical industries demand reliable and precise …
ACP-ADA: a boosting method with data augmentation for improved prediction of anticancer peptides
Cancer is the second-leading cause of death worldwide, and therapeutic peptides that target
and destroy cancer cells have received a great deal of interest in recent years. Traditional …
and destroy cancer cells have received a great deal of interest in recent years. Traditional …
Deep learning algorithm performance evaluation in detection and classification of liver disease using CT images
RV Manjunath, A Ghanshala, K Kwadiki - Multimedia Tools and …, 2024 - Springer
To diagnose the liver diseases computed tomography images are used. Most of the time
even experienced radiologists find it very tough to note the type, size, and severity of the …
even experienced radiologists find it very tough to note the type, size, and severity of the …
GeoDILI: A Robust and Interpretable Model for Drug-Induced Liver Injury Prediction Using Graph Neural Network-Based Molecular Geometric Representation
W Wu, J Qian, C Liang, J Yang, G Ge… - Chemical Research …, 2023 - ACS Publications
Drug-induced liver injury (DILI) is a significant cause of drug failure and withdrawal due to
liver damage. Accurate prediction of hepatotoxic compounds is crucial for safe drug …
liver damage. Accurate prediction of hepatotoxic compounds is crucial for safe drug …
Machine-learning technique, QSAR and molecular dynamics for hERG–drug interactions
One of the most well-known anti-targets defining medication cardiotoxicity is the voltage-
dependent hERG K+ channel, which is well-known for its crucial involvement in cardiac …
dependent hERG K+ channel, which is well-known for its crucial involvement in cardiac …
Predicting non-chemotherapy drug-induced agranulocytosis toxicity through ensemble machine learning approaches
X Huang, X **e, S Huang, S Wu… - Frontiers in Pharmacology, 2024 - frontiersin.org
Agranulocytosis, induced by non-chemotherapy drugs, is a serious medical condition that
presents a formidable challenge in predictive toxicology due to its idiosyncratic nature and …
presents a formidable challenge in predictive toxicology due to its idiosyncratic nature and …