AIMC Topic: Organic Chemicals

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Predicting maturity and identifying key factors in organic waste composting using machine learning models.

Bioresource technology Apr 6, 2024
The measurement of germination index (GI) in composting is a time-consuming and laborious process. This study employed four machine learning (ML) models, namely Random Forest (RF), Artificial Neural Network (ANN), Support Vector Regression (SVR), and...
Machine Learning Temperature Neural Networks, Computer Soil Hydrogen-Ion Concentration Organic Chemicals Germination Composting
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Machine learning modeling of fluorescence spectral data for prediction of trace organic contaminant removal during UV/HO treatment of wastewater.

Water research Mar 18, 2024
Dynamic feedback of the removal performance of trace organic contaminants (TrOCs) is essential towards economical advanced oxidation processes (AOPs), whereas the corresponding quick-response feedback methods have long been desired. Herein, machine l...
Ultraviolet Rays Organic Chemicals Waste Disposal, Fluid Water Pollutants, Chemical Spectrometry, Fluorescence Wastewater Hydrogen Peroxide Machine Learning
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From Organic Fragments to Photoswitchable Catalysts: The OFF-ON Structural Repository for Transferable Kernel-Based Potentials.

Journal of chemical information and modeling Feb 6, 2024
Structurally and conformationally diverse databases are needed to train accurate neural networks or kernel-based potentials capable of exploring the complex free energy landscape of flexible functional organic molecules. Curating such databases for s...
Databases, Factual Peptides Organic Chemicals Neural Networks, Computer Entropy
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The use of simple structural parameters of organic compounds to assess their PUF-air partition coefficients.

Chemosphere Dec 2, 2023
A novel approach is introduced for the reliable prediction of PUF-air partition coefficients of organic compounds, which can determine the environmental fate of organic compounds during interactions with air, soil, and water. The biggest accessible m...
Organic Chemicals Linear Models Neural Networks, Computer Reproducibility of Results Quantitative Structure-Activity Relationship
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Toward a comprehensive understanding of alicyclic compounds: Bio-effects perspective and deep learning approach.

The Science of the total environment Nov 30, 2023
The escalating use of alicyclic compounds in modern industrial production has led to a rapid increase of these substances in the environment, posing significant health hazards. Addressing this challenge necessitates a comprehensive understanding of t...
Deep Learning Organic Chemicals Carbon Industry Neural Networks, Computer
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Improving predictions and understanding of primary and ultimate biodegradation rates with machine learning models.

The Science of the total environment Aug 29, 2023
This study aimed to develop machine learning based quantitative structure biodegradability relationship (QSBR) models for predicting primary and ultimate biodegradation rates of organic chemicals, which are essential parameters for environmental risk...
Linear Models Organic Chemicals Biodegradation, Environmental Software Machine Learning Algorithms
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A Benchmark Study of Graph Models for Molecular Acute Toxicity Prediction.

International journal of molecular sciences Jul 26, 2023
With the wide usage of organic compounds, the assessment of their acute toxicity has drawn great attention to reduce animal testing and human labor. The development of graph models provides new opportunities for acute toxicity prediction. In this stu...
Humans Organic Chemicals Fishes Neural Networks, Computer Benchmarking Aliivibrio fischeri Animals
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Implementing comprehensive machine learning models of multispecies toxicity assessment to improve regulation of organic compounds.

Journal of hazardous materials Jun 27, 2023
Machine learning has made significant progress in assessing the risk associated with hazardous chemicals. However, most models were constructed by randomly selecting one algorithm and one toxicity endpoint towards single species, which may cause bias...
Organic Chemicals Quantitative Structure-Activity Relationship Hazardous Substances Machine Learning Algorithms
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Application of a developed triple-classification machine learning model for carcinogenic prediction of hazardous organic chemicals to the US, EU, and WHO based on Chinese database.

Ecotoxicology and environmental safety Mar 20, 2023
Cancer, the second largest human disease, has become a major public health problem. The prediction of chemicals' carcinogenicity before their synthesis is crucial. In this paper, seven machine learning algorithms (i.e., Random Forest (RF), Logistic R...
Organic Chemicals United States Databases, Factual Carcinogens European Union Machine Learning China Support Vector Machine Bayes Theorem World Health Organization Algorithms Carcinogenesis Hazardous Substances
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Support Vector Machine-Based Global Classification Model of the Toxicity of Organic Compounds to .

Molecules (Basel, Switzerland) Mar 16, 2023
is widely used as the model species in toxicity and risk assessment. For the first time, a global classification model was proposed in this paper for a two-class problem (Class - 1 with log1/IBC ≤ 4.2 and Class + 1 with log1/IBC > 4.2, the unit of I...
Quantitative Structure-Activity Relationship Support Vector Machine Aliivibrio fischeri Software Linear Models Organic Chemicals
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