AIMC Topic: Organic Chemicals

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Prediction of Henry's Law Constants via group-specific quantitative structure property relationships.

Chemosphere Jan 17, 2015
Henry's Law Constants (HLCs) for several hundred organic compounds in water at 25 °C were predicted by Quantitative Structure Property Relationship (QSPR) models, with the division of organic compounds into specific classes to yield more accurate mod...
Water Neural Networks, Computer Gases Quantitative Structure-Activity Relationship Models, Theoretical Linear Models Organic Chemicals
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Predictive modeling and interpretability analysis of bioconcentration factors for organic chemicals in fish using machine learning.

Environmental pollution (Barking, Essex : 1987) Jul 15, 2025
Chemicals are misused and released into the environment, causing adverse effects on people and ecosystems. Assessing the potential environmental risks of these chemicals before their use is crucial. The bioconcentration factor (BCF) is a key paramete...
Bioaccumulation Animals Water Pollutants, Chemical Organic Chemicals Fishes Machine Learning Environmental Monitoring
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Hyperbolic relational graph convolution networks plus: a simple but highly efficient QSAR-modeling method.

Briefings in bioinformatics Sep 2, 2021
Accurate predictions of druggability and bioactivities of compounds are desirable to reduce the high cost and time of drug discovery. After more than five decades of continuing developments, quantitative structure-activity relationship (QSAR) methods...
Organic Chemicals Computational Biology Drug Discovery Quantitative Structure-Activity Relationship Computer Graphics Algorithms Models, Chemical Neural Networks, Computer Drug Design Molecular Structure Artificial Intelligence Computer Simulation
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A machine learning based intramolecular potential for a flexible organic molecule.

Faraday discussions Dec 4, 2020
Quantum mechanical predictive modelling in chemistry and biology is often hindered by the long time scales and large system sizes required of the computational model. Here, we employ the kernel regression machine learning technique to construct an an...
Protein Binding Organic Chemicals Software Monte Carlo Method Thermodynamics Quantum Theory p38 Mitogen-Activated Protein Kinases Machine Learning Epoxide Hydrolases
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A Network Integration Method for Deciphering the Types of Metabolic Pathway of Chemicals with Heterogeneous Information.

Combinatorial chemistry & high throughput screening Jan 1, 2018
AIM AND OBJECTIVE: A metabolic pathway is an important type of biological pathway, which is composed of a series of chemical reactions. It provides essential molecules and energies for living organisms. To date, several metabolic pathways have been u...
Models, Molecular Molecular Structure Structure-Activity Relationship Metabolic Networks and Pathways Support Vector Machine Computational Biology Organic Chemicals
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Improve the biodegradability of post-hydrothermal liquefaction wastewater with ozone: conversion of phenols and N-heterocyclic compounds.

Water science and technology : a journal of the International Association on Water Pollution Research Apr 1, 2017
Hydrothermal liquefaction is a promising technology to convert wet biomass into bio-oil. However, post-hydrothermal liquefaction wastewater (PHWW) is also produced during the process. This wastewater contains a high concentration of organic compounds...
Kinetics Water Pollutants, Chemical Hydroxyl Radical Ozone Heterocyclic Compounds Bioreactors Biomass Wastewater Phenols Organic Chemicals
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