AI Medical Compendium Journal:
Journal of molecular graphics & modelling

Showing 21 to 30 of 42 articles

Machine learning for yield prediction for chemical reactions using in situ sensors.

Journal of molecular graphics & modelling Oct 13, 2022
Machine learning models were developed to predict product formation from time-series reaction data for ten Buchwald-Hartwig coupling reactions. The data was provided by DeepMatter and was collected in their DigitalGlassware cloud platform. The reacti...
Machine Learning Temperature Time Factors
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De novo design of anti-tuberculosis agents using a structure-based deep learning method.

Journal of molecular graphics & modelling Oct 13, 2022
Mycobacterium tuberculosis (Mtb) is a pathogen of major concern due to its ability to withstand both first- and second-line antibiotics, leading to drug resistance. Thus, there is a critical need for identification of novel anti-tuberculosis agents t...
Drug Design Deep Learning Antitubercular Agents Mycobacterium tuberculosis Chorismate Mutase
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A deep learning method for predicting molecular properties and compound-protein interactions.

Journal of molecular graphics & modelling Aug 17, 2022
Predicting molecular properties and compound-protein interactions (CPIs) are two important areas of drug design and discovery. They are also an essential way to discover lead compounds in virtual screening. Recently, in silico methods based on deep l...
Humans Animals Deep Learning Amino Acid Sequence Tumor Suppressor Protein p53 Caenorhabditis elegans
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Graph convolutional neural network applied to the prediction of normal boiling point.

Journal of molecular graphics & modelling Feb 4, 2022
In this article, we describe training and validation of a machine learning model for the prediction of organic compound normal boiling points. Data are drawn from the experimental literature as captured in the NIST Thermodynamics Research Center (TRC...
Neural Networks, Computer Machine Learning Deep Learning
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QSAR and deep learning model for virtual screening of potential inhibitors against Inosine 5' Monophosphate dehydrogenase (IMPDH) of Cryptosporidium parvum.

Journal of molecular graphics & modelling Dec 10, 2021
Cryptosporidium parvum (Cp) causes a gastro-intestinal disease called Cryptosporidiosis. C. parvum Inosine 5' monophosphate dehydrogenase (CpIMPDH) is responsible for the production of guanine nucleotides. In the present study, 37 known urea-based co...
Humans Molecular Docking Simulation Deep Learning Enzyme Inhibitors Quantitative Structure-Activity Relationship IMP Dehydrogenase Inosine Cryptosporidium Cryptosporidiosis Cryptosporidium parvum
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Machine learning and deep learning enabled fuel sooting tendency prediction from molecular structure.

Journal of molecular graphics & modelling Nov 22, 2021
Soot formation models become increasingly important in advanced renewable fuels formulation for soot reduction benefit. This work evaluates performance of machine learning (ML) and deep learning (DL) to predict yield sooting index (YSI) from chemical...
Neural Networks, Computer Machine Learning Deep Learning Molecular Structure Soot
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Assembled graph neural network using graph transformer with edges for protein model quality assessment.

Journal of molecular graphics & modelling Nov 4, 2021
Acquainting protein's structure is of vital importance to accurately understanding its function. Computational method of deep learning has made great progress in protein structure prediction from sequence, and has the potential to help structural bio...
Neural Networks, Computer Proteins
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Machine learning - Predicting Ames mutagenicity of small molecules.

Journal of molecular graphics & modelling Sep 5, 2021
In modern drug discovery, detection of a compound's potential mutagenicity is crucial. However, the traditional method of mutagenicity detection using the Ames test is costly and time consuming as the compounds need to be synthesised and then tested ...
Machine Learning Computer Simulation Support Vector Machine Mutagens Mutagenesis
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Multiple machine learning models combined with virtual screening and molecular docking to identify selective human ALDH1A1 inhibitors.

Journal of molecular graphics & modelling May 28, 2021
Aldehyde dehydrogenases (ALDHs) are the enzymes of oxidoreductase family that are responsible for the aldehyde metabolism. The unbalanced expression of these enzymes may be associated with a variety of disease conditions including cancers. ALDH1A1 is...
Humans Machine Learning Molecular Docking Simulation Aldehyde Dehydrogenase Aldehyde Dehydrogenase 1 Family Retinal Dehydrogenase
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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 ...
Machine Learning Algorithms Organic Chemicals
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