AI Medical Compendium Journal:
Chemical research in toxicology

Showing 31 to 40 of 54 articles

Developing Role for Artificial Intelligence in Drug Discovery in Drug Design, Development, and Safety Assessment.

Chemical research in toxicology
Artificial intelligence (AI) is a rapidly growing discipline in the field of chemical toxicology. Herein, we provide a broad overview of research presented at the Fall 2022 American Chemical Society meeting, highlighting how AI is being applied acros...

In Silico Prediction of Human and Rat Liver Microsomal Stability via Machine Learning Methods.

Chemical research in toxicology
Liver microsomal stability is an important property considered for the screening of drug candidates in the early stage of drug development. Determination of hepatic metabolic stability can be performed by an in vitro assay, but it requires quite a fe...

Machine Learning Models for Predicting Cytotoxicity of Nanomaterials.

Chemical research in toxicology
The wide application of nanomaterials in consumer and medical products has raised concerns about their potential adverse effects on human health. Thus, more and more biological assessments regarding the toxicity of nanomaterials have been performed. ...

Prediction of the Blood-Brain Barrier (BBB) Permeability of Chemicals Based on Machine-Learning and Ensemble Methods.

Chemical research in toxicology
The ability of chemicals to enter the blood-brain barrier (BBB) is a key factor for central nervous system (CNS) drug development. Although many models for BBB permeability prediction have been developed, they have insufficient accuracy (ACC) and sen...

Trade-off Predictivity and Explainability for Machine-Learning Powered Predictive Toxicology: An in-Depth Investigation with Tox21 Data Sets.

Chemical research in toxicology
Selecting a model in predictive toxicology often involves a trade-off between prediction performance and explainability: should we sacrifice the model performance to gain explainability or vice versa. Here we present a comprehensive study to assess a...

Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models.

Chemical research in toxicology
In recent times, machine learning has become increasingly prominent in predictive toxicology as it has shifted from studies toward studies. Currently, methods together with other computational methods such as quantitative structure-activity relati...

DeepDILI: Deep Learning-Powered Drug-Induced Liver Injury Prediction Using Model-Level Representation.

Chemical research in toxicology
Drug-induced liver injury (DILI) is the most frequently reported single cause of safety-related withdrawal of marketed drugs. It is essential to identify drugs with DILI potential at the early stages of drug development. In this study, we describe a ...

Deep Graph Learning with Property Augmentation for Predicting Drug-Induced Liver Injury.

Chemical research in toxicology
Drug-induced liver injury (DILI) is a crucial factor in determining the qualification of potential drugs. However, the DILI property is excessively difficult to obtain due to the complex testing process. Consequently, an screening in the early stage...

Combining Data with Predictions for Modeling Hepatic Steatosis by Using Stratified Bagging and Conformal Prediction.

Chemical research in toxicology
Hepatic steatosis (fatty liver) is a severe liver disease induced by the excessive accumulation of fatty acids in hepatocytes. In this study, we developed reliable models for predicting hepatic steatosis on the basis of an data set of 1041 compound...

Systematic Identification of Molecular Targets and Pathways Related to Human Organ Level Toxicity.

Chemical research in toxicology
The mechanisms leading to organ level toxicities are poorly understood. In this study, we applied an integrated approach to deduce the molecular targets and biological pathways involved in chemically induced toxicity for eight common human organ leve...