AIMC Topic: Organic Chemicals

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Transferable Ring Corrections for Predicting Enthalpy of Formation of Cyclic Compounds.

Journal of chemical information and modeling May 25, 2021
Computational predictions of the thermodynamic properties of molecules and materials play a central role in contemporary reaction prediction and kinetic modeling. Due to the lack of experimental data and computational cost of high-level quantum chemi...
Kinetics Thermodynamics Machine Learning Organic Chemicals
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Support vector machine-based model for toxicity of organic compounds against fish.

Regulatory toxicology and pharmacology : RTP Apr 30, 2021
Predicting the toxicity of chemicals to various fish species through chemometric approach is crucial for ecotoxicological assessment of existing as well as not yet synthesized chemicals. This paper reports a quantitative structure-activity/toxicity r...
Algorithms Fishes Quantitative Structure-Activity Relationship Ecotoxicology Support Vector Machine Water Pollutants, Chemical Animals Organic Chemicals
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ML-DTI: Mutual Learning Mechanism for Interpretable Drug-Target Interaction Prediction.

The journal of physical chemistry letters Apr 27, 2021
Deep learning (DL) provides opportunities for the identification of drug-target interactions (DTIs). The challenges of applying DL lie primarily with the lack of interpretability. Also, most of the existing DL-based methods formulate the drug and tar...
Proteins Pharmaceutical Preparations Protein Binding Organic Chemicals Deep Learning
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Epigenetic Target Fishing with Accurate Machine Learning Models.

Journal of medicinal chemistry Mar 26, 2021
Epigenetic targets are of significant importance in drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represe...
Proof of Concept Study Histone Deacetylases Molecular Structure Databases, Chemical Organic Chemicals Epigenomics Transcription Factors Structure-Activity Relationship Machine Learning Drug Discovery
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Relating molecular descriptors to frontier orbital energy levels, singlet and triplet excited states of fused tricyclics using machine learning.

Journal of molecular graphics & modelling Mar 13, 2021
Fused tricyclic organic compounds are an important class of organic electronic materials. In designing molecules for organic electronics, knowing what chemical structure that be used to tune the molecular property is one of the keys that can help to ...
Organic Chemicals Machine Learning Algorithms
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Towards Deep Neural Network Models for the Prediction of the Blood-Brain Barrier Permeability for Diverse Organic Compounds.

Molecules (Basel, Switzerland) Dec 13, 2020
Permeation through the blood-brain barrier (BBB) is among the most important processes controlling the pharmacokinetic properties of drugs and other bioactive compounds. Using the fragmental (substructural) descriptors representing the occurrence num...
Permeability Humans Neural Networks, Computer Molecular Structure Animals Workflow Algorithms Models, Biological Organic Chemicals Blood-Brain Barrier Biological Transport
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Quantum machine learning using atom-in-molecule-based fragments selected on the fly.

Nature chemistry Sep 14, 2020
First-principles-based exploration of chemical space deepens our understanding of chemistry and might help with the design of new molecules, materials or experiments. Due to the computational cost of quantum chemistry methods and the immense number o...
Molecular Structure Quantum Theory Machine Learning Organic Chemicals
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Quantitative structure-property relationship of distribution coefficients of organic compounds.

SAR and QSAR in environmental research Jul 2, 2020
The -octanol/buffer solution distribution coefficient (or -octanol/water partition coefficient) is of critical importance for measuring lipophilicity of drug candidates. After 4885 molecular descriptor generation, 15 molecular descriptors were select...
Quantitative Structure-Activity Relationship Organic Chemicals Support Vector Machine
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High-Throughput and Autonomous Grazing Incidence X-ray Diffraction Mapping of Organic Combinatorial Thin-Film Library Driven by Machine Learning.

ACS combinatorial science Jun 17, 2020
High-throughput X-ray diffraction (XRD) is one of the most indispensable techniques to accelerate materials research. However, the conventional XRD analysis with a large beam spot size may not best appropriate in a case for characterizing organic mat...
Semiconductors High-Throughput Screening Assays Machine Learning X-Ray Diffraction Organic Chemicals Combinatorial Chemistry Techniques
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Retip: Retention Time Prediction for Compound Annotation in Untargeted Metabolomics.

Analytical chemistry May 21, 2020
Unidentified peaks remain a major problem in untargeted metabolomics by LC-MS/MS. Confidence in peak annotations increases by combining MS/MS matching and retention time. We here show how retention times can be predicted from molecular structures. Tw...
Organic Chemicals Chromatography, Liquid Machine Learning Humans Tandem Mass Spectrometry Metabolomics Time Factors
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