AIMC Topic: Receptors, Cytoplasmic and Nuclear

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Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure.

Genomics Dec 11, 2018
The identification of drug-target interactions has great significance for pharmaceutical scientific research. Since traditional experimental methods identifying drug-target interactions is costly and time-consuming, the use of machine learning method...
Machine Learning Software Drug Development Receptors, Cytoplasmic and Nuclear Ion Channels Position-Specific Scoring Matrices Databases, Protein Receptors, G-Protein-Coupled Protein Conformation
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Prediction of Farnesoid X Receptor Disruptors with Machine Learning Methods.

Chemical research in toxicology Nov 8, 2018
The farnesoid X receptor (FXR) emerges as a promising drug target involved in regulating various metabolic pathways, yet some xenobiotic compounds binding to FXR would be an important determinant to induce the receptor dysfunctions that lead to undes...
Humans Machine Learning Databases, Factual ROC Curve Xenobiotics Area Under Curve Benzimidazoles Receptors, Cytoplasmic and Nuclear
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Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges.

Journal of computer-aided molecular design Aug 16, 2018
Advanced mathematics, such as multiscale weighted colored subgraph and element specific persistent homology, and machine learning including deep neural networks were integrated to construct mathematical deep learning models for pose and binding affin...
Binding Sites Thermodynamics Cathepsins Protein Binding Databases, Protein Protein Kinases Protein Conformation Receptors, Cytoplasmic and Nuclear Ligands Crystallography, X-Ray Computer-Aided Design Drug Design Deep Learning Molecular Docking Simulation
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Chemogenomic Active Learning's Domain of Applicability on Small, Sparse qHTS Matrices: A Study Using Cytochrome P450 and Nuclear Hormone Receptor Families.

ChemMedChem Feb 5, 2018
Computational models for predicting the activity of small molecules against targets are now routinely developed and used in academia and industry, partially due to public bioactivity databases. While models based on bigger datasets are the trend, rec...
Small Molecule Libraries Cytochrome P-450 Enzyme Inhibitors Humans Machine Learning Algorithms Cytochrome P-450 Enzyme System Receptors, Cytoplasmic and Nuclear Databases, Factual Ligands
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Oleanolic acid protects against pathogenesis of atherosclerosis, possibly via FXR-mediated angiotensin (Ang)-(1-7) upregulation.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie Dec 8, 2017
Atherosclerosis, the leading cause of cardiovascular diseases in the world, is a chronic inflammatory disorder characterized by the dysfunction of arteries. Oleanolic acid (OA) is a bioactive nature product which exists in various plants and herbs. P...
Receptors, Cytoplasmic and Nuclear Protective Agents Angiotensins Rabbits Diet, High-Fat Disease Models, Animal Humans Lipoproteins, LDL Up-Regulation Apoptosis Cell Cycle Cytokines Human Umbilical Vein Endothelial Cells Cell Survival Animals Atherosclerosis Oleanolic Acid
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Modern drug design: the implication of using artificial neuronal networks and multiple molecular dynamic simulations.

Journal of computer-aided molecular design Nov 13, 2017
We report the implementation of molecular modeling approaches developed as a part of the 2016 Grand Challenge 2, the blinded competition of computer aided drug design technologies held by the D3R Drug Design Data Resource ( https://drugdesigndata.org...
Humans Molecular Docking Simulation Computer-Aided Design Neural Networks, Computer Machine Learning Ligands Protein Conformation Receptors, Cytoplasmic and Nuclear Drug Design Binding Sites Protein Binding Molecular Dynamics Simulation
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Development of pharmacophore-based classification model for activators of constitutive androstane receptor.

Drug metabolism and pharmacokinetics Nov 17, 2016
Constitutive androstane receptor (CAR) is predominantly expressed in the liver and is important for regulating drug metabolism and transport. Despite its biological importance, there have been few attempts to develop in silico models to predict the a...
Humans Ligands Neural Networks, Computer Receptors, Cytoplasmic and Nuclear Support Vector Machine Constitutive Androstane Receptor
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Identification of New Fungal Peroxisomal Matrix Proteins and Revision of the PTS1 Consensus.

Traffic (Copenhagen, Denmark) Aug 22, 2016
The peroxisomal targeting signal type 1 (PTS1) is a seemingly simple peptide sequence at the C-terminal end of most peroxisomal matrix proteins. PTS1 can be described as a tripeptide with the consensus motif [S/A/C] [K/R/H] L. However, this descripti...
Consensus Sequence Peroxisome-Targeting Signal 1 Receptor Machine Learning Amino Acid Motifs Protein Sorting Signals Genome-Wide Association Study Genome, Fungal Databases, Genetic Receptors, Cytoplasmic and Nuclear Saccharomyces cerevisiae Peroxisomes Saccharomyces cerevisiae Proteins
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Profiling prediction of nuclear receptor modulators with multi-task deep learning methods: toward the virtual screening.

Briefings in bioinformatics Sep 20, 2022
Nuclear receptors (NRs) are ligand-activated transcription factors, which constitute one of the most important targets for drug discovery. Current computational strategies mainly focus on a single target, and the transfer of learned knowledge among N...
Receptor, Metabotropic Glutamate 5 Receptors, Artificial Proto-Oncogene Proteins c-met B-Cell Activation Factor Receptor Neurokinin-1 Receptor Antagonists Calcitonin Receptor-Like Protein Receptors, Calcitriol Receptor-Like Protein Tyrosine Phosphatases, Class 2 Receptors, Muscarinic Cytokine Receptor gp130 Receptors, Aryl Hydrocarbon Receptors, Gastrointestinal Hormone Histamine H2 Antagonists Deep Learning Ligands Receptors, Polymeric Immunoglobulin Receptors, Cytoplasmic and Nuclear
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Application of Machine Learning Methods in Predicting Nuclear Receptors and their Families.

Medicinal chemistry (Shariqah (United Arab Emirates)) Jan 1, 2020
Nuclear receptors (NRs) are a superfamily of ligand-dependent transcription factors that are closely related to cell development, differentiation, reproduction, homeostasis, and metabolism. According to the alignments of the conserved domains, NRs ar...
Machine Learning Animals Computational Biology Receptors, Cytoplasmic and Nuclear Humans
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