AIMC Topic: Carcinogens

Clear Filters Showing 21 to 27 of 27 articles

CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods.

Scientific reports May 18, 2017
Carcinogenicity refers to a highly toxic end point of certain chemicals, and has become an important issue in the drug development process. In this study, three novel ensemble classification models, namely Ensemble SVM, Ensemble RF, and Ensemble XGBo...
Rats Quantitative Structure-Activity Relationship Animals Carcinogens Software Machine Learning
View on PubMed DOI

Novel Uses of In Vitro Data to Develop Quantitative Biological Activity Relationship Models for in Vivo Carcinogenicity Prediction.

Molecular informatics Mar 25, 2015
The availability of large in vitro datasets enables better insight into the mode of action of chemicals and better identification of potential mechanism(s) of toxicity. Several studies have shown that not all in vitro assays can contribute as equal p...
Carcinogens Databases, Factual Models, Biological Machine Learning
View on PubMed DOI

Machine Learning Models Based on Enlarged Chemical Spaces for Screening Carcinogenic Chemicals.

Chemical research in toxicology Jul 21, 2025
Machine learning (ML) models for screening carcinogenic chemicals are critical for the sound management of chemicals. Previous models were built on small-scale datasets and lacked applicability domain (AD) characterization that is necessary for regul...
Humans Algorithms Carcinogens Machine Learning Neural Networks, Computer
View on PubMed DOI

A graph neural network approach for molecule carcinogenicity prediction.

Bioinformatics (Oxford, England) Jun 24, 2022
MOTIVATION: Molecular carcinogenicity is a preventable cause of cancer, but systematically identifying carcinogenic compounds, which involves performing experiments on animal models, is expensive, time consuming and low throughput. As a result, carci...
Carcinogens Mutagens Neural Networks, Computer Animals Forecasting
View on PubMed DOI

PySmash: Python package and individual executable program for representative substructure generation and application.

Briefings in bioinformatics Sep 2, 2021
BACKGROUND: Substructure screening is widely applied to evaluate the molecular potency and ADMET properties of compounds in drug discovery pipelines, and it can also be used to interpret QSAR models for the design of new compounds with desirable phys...
Software Drug Discovery Machine Learning Humans Carcinogenicity Tests Drug Screening Assays, Antitumor Carcinogens Computational Biology
View on PubMed DOI

Evaluation of genotoxicity after acute and chronic exposure to 2,4-dichlorophenoxyacetic acid herbicide (2,4-D) in rodents using machine learning algorithms.

The Journal of toxicological sciences Jan 1, 2020
2,4-Dichlorophenoxyacetic acid (2,4-D) is one of the most widely used herbicides in the world, but its mutagenic and carcinogenic potential is still controversial. We simulated environmental exposure to 2,4-D, with the objective of evaluating the gen...
2,4-Dichlorophenoxyacetic Acid Carcinogens Mutagenicity Tests Mutagens Comet Assay Micronucleus Tests Inhalation Exposure Environmental Exposure Herbicides DNA Damage Time Factors Male Rats, Wistar Mice Algorithms Animals Machine Learning
View on PubMed DOI

[Construction of a High-precision Chemical Prediction System Using Human ESCs].

Yakugaku zasshi : Journal of the Pharmaceutical Society of Japan Jan 1, 2018
　Toxicity prediction based on stem cells and tissue derived from stem cells plays a very important role in the fields of biomedicine and pharmacology. Here we report on qRT-PCR data obtained by exposing 20 compounds to human embryonic stem (ES) cells...
Embryonic Stem Cells Carcinogens Permethrin Neurotoxins Quantitative Structure-Activity Relationship Toxicity Tests Benzhydryl Compounds Support Vector Machine Phenols Humans
View on PubMed DOI
Popular Topics
Recent Journals