AIMC Topic: Organic Chemicals

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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...
Machine Learning Organic Chemicals Hydrogen Thermodynamics Oxygen Carbon
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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...
Supervised Machine Learning Organic Chemicals Neural Networks, Computer
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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...
Machine Learning Algorithms Metabolomics Chromatography, Liquid Mass Spectrometry Organic Chemicals Databases, Chemical Bayes Theorem Molecular Structure Linear Models
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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...
Binding Sites Drug Discovery Isomerism Regression Analysis Neural Networks, Computer Models, Theoretical Quantitative Structure-Activity Relationship Area Under Curve Organic Chemicals Databases as Topic ROC Curve
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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, ...
Machine Learning Organic Chemicals
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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...
Molecular Structure Hydrogen Bonding Density Functional Theory Machine Learning Organic Chemicals
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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...
Neural Networks, Computer Molecular Dynamics Simulation Organic Chemicals Polymers
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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...
Machine Learning Quantum Theory Organic Chemicals Inorganic Chemicals
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Towards a better understanding of deep convolutional neural network processes for recognizing organic chemicals of environmental concern.

Journal of hazardous materials Jul 27, 2021
Deep convolutional neural network (DCNN) has proved to be a promising tool for identifying organic chemicals of environmental concern. However, the uncertainty associated with DCNN predictions remains to be quantified. The training process contains m...
Organic Chemicals Neural Networks, Computer
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Prediction of the Blood-Brain Barrier (BBB) Permeability of Chemicals Based on Machine-Learning and Ensemble Methods.

Chemical research in toxicology May 28, 2021
The ability of chemicals to enter the blood-brain barrier (BBB) is a key factor for central nervous system (CNS) drug development. Although many models for BBB permeability prediction have been developed, they have insufficient accuracy (ACC) and sen...
Organic Chemicals Machine Learning Permeability Humans Blood-Brain Barrier
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