AIMC Topic: Organic Chemicals

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Identifying Organic Chemicals with Acetylcholinesterase Inhibition in Nationwide Estuarine Waters by Machine Learning-Assisted Mass Spectrometric Screening.

Environmental science & technology Dec 4, 2024
Neurotoxicity is frequently observed in the global aquatic environment, threatening aquatic ecosystems and human health. However, a very limited proportion of neurotoxic effects (∼1%) has been explained by known chemicals of concern. Here, we integra...
China Organic Chemicals Environmental Monitoring Machine Learning Acetylcholinesterase Cholinesterase Inhibitors Estuaries Mass Spectrometry Water Pollutants, Chemical
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Construction of interpretable ensemble learning models for predicting bioaccumulation parameters of organic chemicals in fish.

Journal of hazardous materials Nov 20, 2024
Accurate prediction of bioaccumulation parameters is essential for assessing exposure, hazards, and risks of chemicals. However, the majority of prediction models on bioaccumulation parameters are individual models based on a single algorithm and lac...
Models, Theoretical Water Pollutants, Chemical Fishes Animals Organic Chemicals Ensemble Learning Bioaccumulation Machine Learning Algorithms
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Insights into the characteristics and toxicity of microalgal biochar-derived dissolved organic matter by spectroscopy and machine learning.

The Science of the total environment Nov 20, 2024
Microalgal biochar has potential applications in various fields; however, there is limited research on the properties and risks of microalgal biochar-derived dissolved organic matter (MBDOM). This study examined how different pyrolysis temperatures (...
Machine Learning Organic Chemicals Water Pollutants, Chemical Microalgae Phosphorus Spectrum Analysis Charcoal Nitrogen
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Chemoinformatics for corrosion science: Data-driven modeling of corrosion inhibition by organic molecules.

Molecular informatics Oct 15, 2024
This paper reviews the application of machine learning to the inhibition of corrosion by organic molecules. The methodologies considered include quantitative structure-property relationships (QSPR) and related data-driven approaches. The characterist...
Corrosion Machine Learning Organic Chemicals Quantitative Structure-Activity Relationship Cheminformatics
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Machine learning-based q-RASAR predictions of the bioconcentration factor of organic molecules estimated following the organisation for economic co-operation and development guideline 305.

Journal of hazardous materials Sep 3, 2024
In this study, we utilized an innovative quantitative read-across (RA) structure-activity relationship (q-RASAR) approach to predict the bioconcentration factor (BCF) values of a diverse range of organic compounds, based on a dataset of 575 compounds...
Quantitative Structure-Activity Relationship Bioaccumulation Organisation for Economic Co-Operation and Development Water Pollutants, Chemical Fishes Animals Organic Chemicals Machine Learning Algorithms
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Thermodynamics and explainable machine learning assist in interpreting biodegradability of dissolved organic matter in sludge anaerobic digestion with thermal hydrolysis.

Bioresource technology Aug 28, 2024
Dissolved organic matter (DOM) is essential in biological treatment, yet its specific roles remain incompletely understood. This study introduces a machine learning (ML) framework to interpret DOM biodegradability in the anaerobic digestion (AD) of s...
Machine Learning Hydrolysis Sewage Anaerobiosis Thermodynamics Nitrogen Organic Chemicals Biodegradation, Environmental Carbon
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Meta-Analysis and Machine Learning Models for Anaerobic Biodegradation Rates of Organic Contaminants in Sediments and Sludge.

Environmental science & technology Jul 10, 2024
Anaerobic biodegradation rates (half-lives) of organic chemicals are pivotal for environmental risk assessment and remediation. Traditional experimental evaluation, constrained by prolonged, oxygen-free conditions, struggles to keep pace with emergin...
Anaerobiosis Machine Learning Geologic Sediments Sewage Biodegradation, Environmental Organic Chemicals
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Understanding the phytotoxic effects of organic contaminants on rice through predictive modeling with molecular descriptors: A data-driven analysis.

Journal of hazardous materials Jun 17, 2024
The widespread introduction of organic compounds into environments poses significant risks to ecosystems. Assessing the adverse effects of organic contaminants on crops is crucial for ensuring food safety. However, laboratory research is often time-c...
Oryza Photosynthesis Oxidative Stress Machine Learning Organic Chemicals Soil Pollutants
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Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling.

Environmental science & technology May 26, 2024
In recent years, alternative animal testing methods such as computational and machine learning approaches have become increasingly crucial for toxicity testing. However, the complexity and scarcity of available biomedical data challenge the developme...
Quantitative Structure-Activity Relationship Animals Neural Networks, Computer Toxicity Tests Organic Chemicals Machine Learning
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Predicting drug solubility in organic solvents mixtures: A machine-learning approach supported by high-throughput experimentation.

International journal of pharmaceutics May 18, 2024
A novel approach based on supervised machine-learning is proposed to predict the solubility of drugs and drug-like molecules in mixtures of organic solvents. Similar to quantitative structure-property relationship (QSPR) models, different solvent typ...
Solubility Solvents High-Throughput Screening Assays Temperature Least-Squares Analysis Quantitative Structure-Activity Relationship Organic Chemicals Pharmaceutical Preparations Machine Learning
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