A5028184276- Chemistry and Chemical Engineering
- Computational Drug Discovery Methods
- History and advancements in chemistry
- Process Optimization and Integration
- Environmental Impact and Sustainability
- Bacillus and Francisella bacterial research
- Graphene and Nanomaterials Applications
- Various Chemistry Research Topics
- Machine Learning in Materials Science
- Microplastics and Plastic Pollution
- Risk and Safety Analysis
- Nanoparticles: synthesis and applications
- Cholinesterase and Neurodegenerative Diseases
- Analytical Chemistry and Chromatography
- Sustainable Development and Environmental Policy
- Chemical Safety and Risk Management
- Carcinogens and Genotoxicity Assessment
- Advanced Statistical Methods and Models
- Spectroscopy and Chemometric Analyses
- Recycling and Waste Management Techniques
- Sustainable Supply Chain Management
- Metabolomics and Mass Spectrometry Studies
Environmental Protection Agency
2002-2024
Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of true data set, best way validate ability is perform statistical validation. validation, overall set divided into training test sets. Commonly, this splitting performed random division. Rational methods can divide sets in an intelligent fashion. The purpose study was determine whether rational division lead more models...
Abstract Quantitative structure–activity relationships (QSARs) are used to predict many different endpoints, utilize hundreds, and even thousands of parameters (or descriptors), created using a variety approaches. The one thing they all have in common is the assumption that chemical structures correct. This research investigates this by examining six public private databases contain structural information for chemicals. Molecular fingerprinting techniques determine error rates each...
ABSTRACT A quantitative structure-activity relationship (QSAR) methodology based on hierarchical clustering was developed to predict toxicological endpoints. This utilizes Ward's method divide a training set into series of structurally similar clusters. The structural similarity is defined in terms 2-D physicochemical descriptors (such as connectivity and E-state indices). genetic algorithm-based technique used generate statistically valid QSAR models for each cluster (using the pool...
Abstract PARIS III (Program for Assisting the Replacement of Industrial Solvents III, Version 1.4.0) is a pollution prevention solvent substitution software tool used to find mixtures solvents that are less harmful environment than industrial be replaced. By searching extensively though hundreds millions possible combinations, perform same as original may found. Greener substitutes then chosen from those behave similarly but have environmental impact. These extensive searches enhanced by...
Solvents used throughout industry are chosen to meet specific technological requirements such as solute solubility, cleaning and degreasing ability, or utility a medium for paints coatings. With the increasing awareness of human health effects environmental risks solvent use, replacement current solvents with more benign alternatives has become necessity. In this paper, we initially outline elements design theory, on basis then discuss PARIS II (Program Assisting Replacement Industrial...
With continued development of new chemicals and genetically engineered microbes as potential agents for terrorism industrial development, there is a great need the application quantitative structure activity relationships (QSARs) virulence factor (VFARs). Development QSARs VFARs will facilitate efficient streamlined use dwindling resources assessment risks associated with exposures to chemical biological agents. To at US Environmental Protection Agency, two day workshop was organized June...
The EPA nanoQSAR model predicts the impacts of in vitro cell viability following exposure to certain nanomaterials.