AI Medical Compendium Journal:
SAR and QSAR in environmental research

Showing 31 to 40 of 46 articles

Classification of sphingosine kinase inhibitors using counter propagation artificial neural networks: A systematic route for designing selective SphK inhibitors.

SAR and QSAR in environmental research
Accurate and robust classification models for describing and predicting the activity of 330 chemicals that are sphingosine kinase 1 (SphK1) and/or sphingosine kinase 2 (SphK2) inhibitors were derived. The classification models developed in this work ...

QSPR studies for predicting polarity parameter of organic compounds in methanol using support vector machine and enhanced replacement method.

SAR and QSAR in environmental research
In the present work, enhanced replacement method (ERM) and support vector machine (SVM) were used for quantitative structure-property relationship (QSPR) studies of polarity parameter (p) of various organic compounds in methanol in reversed phase liq...

Investigation of the influence of protein corona composition on gold nanoparticle bioactivity using machine learning approaches.

SAR and QSAR in environmental research
The understanding of the mechanisms and interactions that occur when nanomaterials enter biological systems is important to improve their future use. The adsorption of proteins from biological fluids in a physiological environment to form a corona on...

Robust modelling of acute toxicity towards fathead minnow (Pimephales promelas) using counter-propagation artificial neural networks and genetic algorithm.

SAR and QSAR in environmental research
Large worldwide use of chemicals has caused great concern about their possible adverse effects on human health, flora and fauna. Increased production of new chemicals has also increased demand for their risk assessment. Traditionally, results from an...

Development of an informatics infrastructure for data exchange of biomolecular simulations: Architecture, data models and ontology.

SAR and QSAR in environmental research
Biomolecular simulations aim to simulate structure, dynamics, interactions, and energetics of complex biomolecular systems. With the recent advances in hardware, it is now possible to use more complex and accurate models, but also reach time scales t...

An in silico expert system for the identification of eye irritants.

SAR and QSAR in environmental research
This report describes development of an in silico, expert rule-based method for the classification of chemicals into irritants or non-irritants to eye, as defined by the Draize test. This method was developed to screen data-poor cosmetic ingredient c...

Three- and four-class classification models for P-glycoprotein inhibitors using counter-propagation neural networks.

SAR and QSAR in environmental research
P-glycoprotein (P-gp) is an ATP binding cassette (ABC) transporter that helps to protect several certain human organs from xenobiotic exposure. This efflux pump is also responsible for multi-drug resistance (MDR), an issue of the chemotherapy approac...

Comparison of predictions of developmental toxicity for compounds of solvent data set.

SAR and QSAR in environmental research
We have considered a series of 235 compounds technically classified as solvents. Chemically, they belong to different classes. Their potential developmental toxicity was evaluated using two models available on platform VEGA HUB; model CAESAR and the ...

A modified binary particle swarm optimization with a machine learning algorithm and molecular docking for QSAR modelling of cholinesterase inhibitors.

SAR and QSAR in environmental research
The acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) inhibitors play a key role in treating Alzheimer's disease. This study proposes an approach that integrates a modified binary particle swarm optimization (PSO) with a machine learning ...

Filter feature selectors in the development of binary QSAR models.

SAR and QSAR in environmental research
The application of machine learning methods to the construction of quantitative structure-activity relationship models is a complex computational problem in which dimensionality reduction of the representation of the molecular structure plays a funda...