AIMC Topic: 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 Ligands Endocrine Disruptors Receptors, Estrogen Receptors, Cytoplasmic and Nuclear Deep Learning Estrogens Receptors, G-Protein-Coupled
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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...
Receptors, Cytoplasmic and Nuclear Computer Simulation Deep Learning Models, Statistical Pharmaceutical Preparations Humans Quantitative Structure-Activity Relationship Algorithms Toxicity Tests
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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...
Support Vector Machine Databases, Protein Receptors, G-Protein-Coupled Drug Development Wavelet Analysis Ion Channels Receptors, Cytoplasmic and Nuclear Enzymes
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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...
Coronavirus 3C Proteases Coronavirus Papain-Like Proteases Karyopherins Protein Conformation Receptors, Cytoplasmic and Nuclear Binding Sites Protein Tyrosine Phosphatase, Non-Receptor Type 1 Exportin 1 Protein Area Under Curve Antigens, CD Molecular Dynamics Simulation Humans Interleukin-2 Hepacivirus Machine Learning ROC Curve Models, Statistical Ligands Algorithms Computational Biology Regression Analysis Elasticity Reproducibility of Results Antigens, Neoplasm SARS-CoV-2
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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...
Algorithms Deep Learning Receptors, Cytoplasmic and Nuclear Neural Networks, Computer
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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...
Machine Learning Algorithms Computer Simulation Drug Discovery Drug Development Databases, Protein Databases, Chemical Ion Channels Receptors, G-Protein-Coupled Receptors, Cytoplasmic and Nuclear Pharmaceutical Preparations Enzymes Amino Acid Sequence
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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...
Algorithms Small Molecule Libraries Ligands Constitutive Androstane Receptor Reproducibility of Results Quantitative Structure-Activity Relationship Receptors, Cytoplasmic and Nuclear Drug Discovery Deep Learning
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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...
Ion Channels Position-Specific Scoring Matrices Receptors, G-Protein-Coupled Protein Conformation Receptors, Cytoplasmic and Nuclear Machine Learning Software Drug Development Databases, Protein
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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...
Receptors, Cytoplasmic and Nuclear Xenobiotics Humans ROC Curve Machine Learning Area Under Curve Databases, Factual Benzimidazoles
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