AI Medical Compendium Journal:
Chemical research in toxicology

Showing 1 to 10 of 54 articles

Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.

Chemical research in toxicology
Machine learning (ML) is increasingly valuable for predicting molecular properties and toxicity in drug discovery. However, toxicity-related end points have always been challenging to evaluate experimentally with respect to translation due to the re...

Data Exploration for Target Predictions Using Proprietary and Publicly Available Data Sets.

Chemical research in toxicology
When applying machine learning (ML) approaches for the prediction of bioactivity, it is common to collect data from different assays or sources and combine them into single data sets. However, depending on the data domains and sources from which thes...

BoostDILI: Extreme Gradient Boost-Powered Drug-Induced Liver Injury Prediction and Structural Alerts Generation.

Chemical research in toxicology
Over the past 60 years, drug-induced liver injury (DILI) has played a key role in the withdrawal of marketed drugs due to safety concerns. Early prediction of DILI is crucial for developing safer pharmaceuticals, yet current and testing methods are...

DICTrank Is a Reliable Dataset for Cardiotoxicity Prediction Using Machine Learning Methods.

Chemical research in toxicology
Drug-induced cardiotoxicity (DICT) is a significant challenge in drug development and public health. DICT can arise from various mechanisms; New Approach Methods (NAMs), including quantitative structure-activity relationships (QSARs), have been exten...

Predicting Liver-Related In Vitro Endpoints with Machine Learning to Support Early Detection of Drug-Induced Liver Injury.

Chemical research in toxicology
Drug-induced liver injury (DILI) is a major cause of drug development failures and postmarket drug withdrawals, posing significant challenges to public health and pharmaceutical research. The biological mechanisms leading to DILI are highly complex a...

Elevating Research and Careers in the Development of Safer Drugs through Artificial Intelligence.

Chemical research in toxicology
The session "Elevating Research and Careers in the Development of Safer Drugs through Artificial Intelligence," held at the American Chemical Society meeting, showcased innovative methodologies that AI brings to drug development, from predictive mode...

Machine Learning-Based Prediction of the Inhibitory Activity of Chemical Substances Against Rat and Human Cytochrome P450s.

Chemical research in toxicology
The prediction of cytochrome P450 inhibition by a computational (quantitative) structure-activity relationship approach using chemical structure information and machine learning would be useful for toxicity research as a simple and rapid tool. Howev...

Environmentally Sustainable Detection of Arsenic using Convolutional Neural Networks and Imidazole-Based Organic Probes: Application in Food Samples and Arsenic Album.

Chemical research in toxicology
Arsenic contamination poses a significant health risk, particularly when it infiltrates water supplies. While current detection methods offer precise analysis, they often involve complex instrumentation not suitable for field use. This study presents...

Prediction of Cytochrome P450 Substrates Using the Explainable Multitask Deep Learning Models.

Chemical research in toxicology
Cytochromes P450 (P450s or CYPs) are the most important phase I metabolic enzymes in the human body and are responsible for metabolizing ∼75% of the clinically used drugs. P450-mediated metabolism is also closely associated with the formation of toxi...

Toward an Explainable Large Language Model for the Automatic Identification of the Drug-Induced Liver Injury Literature.

Chemical research in toxicology
Drug-induced liver injury (DILI) stands as a significant concern in drug safety, representing the primary cause of acute liver failure. Identifying the scientific literature related to DILI is crucial for monitoring, investigating, and conducting met...