AIMC Topic: Organic 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...
Neural Networks, Computer Organic Chemicals
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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...
Permeability Machine Learning Humans Organic Chemicals Blood-Brain Barrier
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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...
Machine Learning Organic Chemicals Thermodynamics Kinetics
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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...
Water Pollutants, Chemical Fishes Support Vector Machine Ecotoxicology Quantitative Structure-Activity Relationship Algorithms Organic Chemicals Animals
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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...
Deep Learning Protein Binding Proteins Organic Chemicals Pharmaceutical Preparations
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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...
Histone Deacetylases Drug Discovery Machine Learning Epigenomics Molecular Structure Structure-Activity Relationship Proof of Concept Study Transcription Factors Databases, Chemical Organic Chemicals
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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 Biological Transport Workflow Models, Biological Humans Molecular Structure Neural Networks, Computer Animals Organic Chemicals Blood-Brain Barrier Algorithms
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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...
Quantum Theory Machine Learning Molecular Structure 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...
Support Vector Machine Quantitative Structure-Activity Relationship Organic Chemicals
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