AIMC Topic: Receptors, Cytoplasmic and Nuclear

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Nondisruptive inducible labeling of ER-membrane contact sites using the Lamin B receptor.

PLoS biology Jul 10, 2025
Membrane contact sites (MCSs) are areas of close proximity between organelles that allow the exchange of material, among other roles. The endoplasmic reticulum (ER) has MCSs with a variety of organelles in the cell. MCSs are dynamic, responding to ch...
Lamin B Receptor Intracellular Membranes Mitosis Golgi Apparatus Endoplasmic Reticulum Humans Staining and Labeling Lysosomes HeLa Cells Cell Membrane Receptors, Cytoplasmic and Nuclear
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From Nuclear Receptors to GPCRs: a Deep Transfer Learning Approach for Enhanced Environmental Estrogen Recognition.

Environmental science & technology Apr 30, 2025
Environmental estrogens (EEs), as typical endocrine-disrupting chemicals (EDCs), can bind to classic estrogen receptors (ERs) to induce genomic effects, as well as to G protein-coupled estrogen receptor (GPER) located on the membrane, thereby inducin...
Humans Deep Learning Estrogens Receptors, G-Protein-Coupled Ligands Endocrine Disruptors Receptors, Estrogen Receptors, Cytoplasmic and Nuclear
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Integrating deep learning and molecular dynamics simulations for FXR antagonist discovery.

Molecular diversity Apr 2, 2025
Farnesoid X receptor (FXR) is a key regulator of bile acid, lipid, and glucose homeostasis, making it a promising target for treating metabolic diseases. FXR antagonists have shown therapeutic potential in cholestasis, metabolic disorders, and certai...
Humans Molecular Dynamics Simulation Hydrophobic and Hydrophilic Interactions Receptors, Cytoplasmic and Nuclear Thermodynamics Drug Discovery Deep Learning Protein Binding
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Determination of Molecule Category of Ligands Targeting the Ligand-Binding Pocket of Nuclear Receptors with Structural Elucidation and Machine Learning.

Journal of chemical information and modeling Aug 30, 2022
The mechanism of transcriptional activation/repression of the nuclear receptors (NRs) involves two main conformations of the NR protein, namely, the active (agonistic) and inactive (antagonistic) conformations. Binding of agonists or antagonists to t...
Receptors, Cytoplasmic and Nuclear Binding Sites Machine Learning Ligands
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Prediction Models for Agonists and Antagonists of Molecular Initiation Events for Toxicity Pathways Using an Improved Deep-Learning-Based Quantitative Structure-Activity Relationship System.

International journal of molecular sciences Oct 6, 2021
In silico approaches have been studied intensively to assess the toxicological risk of various chemical compounds as alternatives to traditional in vivo animal tests. Among these approaches, quantitative structure-activity relationship (QSAR) analysi...
Pharmaceutical Preparations Quantitative Structure-Activity Relationship Toxicity Tests Computer Simulation Deep Learning Models, Statistical Receptors, Cytoplasmic and Nuclear Humans Algorithms
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Prediction of Drug-Target Interactions by Combining Dual-Tree Complex Wavelet Transform with Ensemble Learning Method.

Molecules (Basel, Switzerland) Sep 3, 2021
Identification of drug-target interactions (DTIs) is vital for drug discovery. However, traditional biological approaches have some unavoidable shortcomings, such as being time consuming and expensive. Therefore, there is an urgent need to develop no...
Wavelet Analysis Receptors, Cytoplasmic and Nuclear Enzymes Ion Channels Support Vector Machine Databases, Protein Drug Development Receptors, G-Protein-Coupled
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Predicting cryptic ligand binding sites based on normal modes guided conformational sampling.

Proteins Dec 5, 2020
To greatly expand the druggable genome, fast and accurate predictions of cryptic sites for small molecules binding in target proteins are in high demand. In this study, we have developed a fast and simple conformational sampling scheme guided by norm...
SARS-CoV-2 ROC Curve Protein Tyrosine Phosphatase, Non-Receptor Type 1 Computational Biology Exportin 1 Protein Antigens, Neoplasm Interleukin-2 Elasticity Models, Statistical Protein Conformation Machine Learning Molecular Dynamics Simulation Regression Analysis Receptors, Cytoplasmic and Nuclear Coronavirus Papain-Like Proteases Humans Algorithms Hepacivirus Area Under Curve Binding Sites Karyopherins Reproducibility of Results Ligands Antigens, CD Coronavirus 3C Proteases
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The Effect of Resampling on Data-imbalanced Conditions for Prediction towards Nuclear Receptor Profiling Using Deep Learning.

Molecular informatics Mar 31, 2020
In toxicity evaluation based on the nuclear receptor signalling pathway, in silico prediction tools are used for the detection of the early stages of long-term toxicities, the prioritization of newly synthesized chemicals and the acquisition of the s...
Neural Networks, Computer Algorithms Deep Learning Receptors, Cytoplasmic and Nuclear
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A Machine Learning Approach for Drug-target Interaction Prediction using Wrapper Feature Selection and Class Balancing.

Molecular informatics Feb 11, 2020
Drug-Target interaction (DTI) plays a crucial role in drug discovery, drug repositioning and understanding the drug side effects which helps to identify new therapeutic profiles for various diseases. However, the exponential growth in the genomic and...
Enzymes Ion Channels Computer Simulation Amino Acid Sequence Drug Discovery Databases, Protein Machine Learning Databases, Chemical Drug Development Receptors, G-Protein-Coupled Algorithms Receptors, Cytoplasmic and Nuclear Pharmaceutical Preparations
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Prediction Model with High-Performance Constitutive Androstane Receptor (CAR) Using DeepSnap-Deep Learning Approach from the Tox21 10K Compound Library.

International journal of molecular sciences Sep 30, 2019
The constitutive androstane receptor (CAR) plays pivotal roles in drug-induced liver injury through the transcriptional regulation of drug-metabolizing enzymes and transporters. Thus, identifying regulatory factors for CAR activation is important for...
Receptors, Cytoplasmic and Nuclear Algorithms Deep Learning Small Molecule Libraries Reproducibility of Results Ligands Constitutive Androstane Receptor Drug Discovery Quantitative Structure-Activity Relationship
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