AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Tetrahymena pyriformis

Showing 1 to 4 of 4 articles

Clear Filters

Quantitative Regression Models for the Prediction of Chemical Properties by an Efficient Workflow.

Molecular informatics
Rapid safety assessment is more and more needed for the increasing chemicals both in chemical industries and regulators around the world. The traditional experimental methods couldn't meet the current demand any more. With the development of the info...

Prediction of the aquatic toxicity of aromatic compounds to tetrahymena pyriformis through support vector regression.

Oncotarget
Toxicity evaluation is an extremely important process during drug development. It is usually initiated by experiments on animals, which is time-consuming and costly. To speed up such a process, a quantitative structure-activity relationship (QSAR) st...

Machine learning-based models to predict modes of toxic action of phenols to Tetrahymena pyriformis.

SAR and QSAR in environmental research
The phenols are structurally heterogeneous pollutants and they present a variety of modes of toxic action (MOA), including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles, and soft electrophiles. Because it is often difficult to ...

Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets.

Briefings in bioinformatics
Although a wide variety of machine learning (ML) algorithms have been utilized to learn quantitative structure-activity relationships (QSARs), there is no agreed single best algorithm for QSAR learning. Therefore, a comprehensive understanding of the...