AIMC Topic: Organic Chemicals

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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...
Carcinogenesis Bayes Theorem United States Support Vector Machine Algorithms Carcinogens Machine Learning Databases, Factual European Union World Health Organization China Hazardous Substances Organic Chemicals
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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...
Organic Chemicals Support Vector Machine Software Aliivibrio fischeri Linear Models Quantitative Structure-Activity Relationship
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Machine Learning for Predicting Chemical Potentials of Multifunctional Organic Compounds in Atmospherically Relevant Solutions.

The journal of physical chemistry letters Oct 19, 2022
We have trained the Extreme Minimum Learning Machine (EMLM) machine learning model to predict chemical potentials of individual conformers of multifunctional organic compounds containing carbon, hydrogen, and oxygen. The model is able to predict chem...
Oxygen Hydrogen Organic Chemicals Carbon Thermodynamics Machine Learning
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Improving Chemical Reaction Prediction with Unlabeled Data.

Molecules (Basel, Switzerland) Sep 14, 2022
Predicting products of organic chemical reactions is useful in chemical sciences, especially when one or more reactants are new organics. However, the performance of traditional learning models heavily relies on high-quality labeled data. In this wor...
Neural Networks, Computer Organic Chemicals Supervised Machine Learning
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Quantitative structure retention relationship (QSRR) modelling for Analytes' retention prediction in LC-HRMS by applying different Machine Learning algorithms and evaluating their performance.

Journal of chromatography. B, Analytical technologies in the biomedical and life sciences Jan 19, 2022
In metabolomics, retention prediction methods have been developed based on the structural and physicochemical characteristics of analytes. Such methods employ regression models, harnessing machine learning algorithms mapping experimentally derived re...
Linear Models Databases, Chemical Molecular Structure Bayes Theorem Organic Chemicals Metabolomics Machine Learning Mass Spectrometry Chromatography, Liquid Algorithms
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CRNNTL: Convolutional Recurrent Neural Network and Transfer Learning for QSAR Modeling in Organic Drug and Material Discovery.

Molecules (Basel, Switzerland) Nov 30, 2021
Molecular latent representations, derived from autoencoders (AEs), have been widely used for drug or material discovery over the past couple of years. In particular, a variety of machine learning methods based on latent representations have shown exc...
Regression Analysis Area Under Curve Isomerism Databases as Topic Organic Chemicals Models, Theoretical Drug Discovery ROC Curve Quantitative Structure-Activity Relationship Binding Sites Neural Networks, Computer
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Topology Automated Force-Field Interactions (TAFFI): A Framework for Developing Transferable Force Fields.

Journal of chemical information and modeling Sep 17, 2021
Force-field development has undergone a revolution in the past decade with the proliferation of quantum chemistry based parametrizations and the introduction of machine learning approximations of the atomistic potential energy surface. Nevertheless, ...
Organic Chemicals Machine Learning
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Combining machine learning and quantum mechanics yields more chemically aware molecular descriptors for medicinal chemistry applications.

Journal of computational chemistry Aug 19, 2021
Molecular interaction fields (MIFs), describing molecules in terms of their ability to interact with any chemical entity, are one of the most established and versatile concepts in drug discovery. Improvement of this molecular description is highly de...
Hydrogen Bonding Machine Learning Organic Chemicals Density Functional Theory Molecular Structure
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A Scalable Graph Neural Network Method for Developing an Accurate Force Field of Large Flexible Organic Molecules.

The journal of physical chemistry letters Aug 16, 2021
An accurate force field is the key to the success of all molecular mechanics simulations on organic polymers and biomolecules. Accurate correlated wave function (CW) methods scale poorly with system size, so this poses a great challenge to the develo...
Polymers Neural Networks, Computer Molecular Dynamics Simulation Organic Chemicals
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Ab Initio Machine Learning in Chemical Compound Space.

Chemical reviews Aug 13, 2021
Chemical compound space (CCS), the set of all theoretically conceivable combinations of chemical elements and (meta-)stable geometries that make up matter, is colossal. The first-principles based virtual sampling of this space, for example, in search...
Organic Chemicals Quantum Theory Machine Learning Inorganic Chemicals
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