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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 30550813
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...
Databases, Protein Drug Development Ion Channels Machine Learning Position-Specific Scoring Matrices Protein Conformation Receptors, Cytoplasmic and Nuclear Receptors, G-Protein-Coupled Software
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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 31574921
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 Constitutive Androstane Receptor Deep Learning Drug Discovery Ligands Quantitative Structure-Activity Relationship Receptors, Cytoplasmic and Nuclear Reproducibility of Results Small Molecule Libraries
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Application of Machine Learning Methods in Predicting Nuclear Receptors and their Families.

Medicinal chemistry (Shariqah (United Arab Emirates)) 31584374
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...
Animals Computational Biology Humans Machine Learning Receptors, Cytoplasmic and Nuclear
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The Effect of Resampling on Data-imbalanced Conditions for Prediction towards Nuclear Receptor Profiling Using Deep Learning.

Molecular informatics 31985163
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 Neural Networks, Computer 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 32003548
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...
Algorithms Amino Acid Sequence Computer Simulation Databases, Chemical Databases, Protein Drug Development Drug Discovery Enzymes Ion Channels Machine Learning Pharmaceutical Preparations Receptors, Cytoplasmic and Nuclear Receptors, G-Protein-Coupled
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Predicting cryptic ligand binding sites based on normal modes guided conformational sampling.

Proteins 33244830
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...
Algorithms Antigens, CD Antigens, Neoplasm Area Under Curve Binding Sites Computational Biology Coronavirus 3C Proteases Coronavirus Papain-Like Proteases Elasticity Exportin 1 Protein Hepacivirus Humans Interleukin-2 Karyopherins Ligands Machine Learning Models, Statistical Molecular Dynamics Simulation Protein Conformation Protein Tyrosine Phosphatase, Non-Receptor Type 1 Receptors, Cytoplasmic and Nuclear Regression Analysis Reproducibility of Results ROC Curve SARS-CoV-2
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Prediction of Drug-Target Interactions by Combining Dual-Tree Complex Wavelet Transform with Ensemble Learning Method.

Molecules (Basel, Switzerland) 34500792
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...
Databases, Protein Drug Development Enzymes Ion Channels Receptors, Cytoplasmic and Nuclear Receptors, G-Protein-Coupled Support Vector Machine Wavelet Analysis
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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 34639159
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...
Algorithms Computer Simulation Deep Learning Humans Models, Statistical Pharmaceutical Preparations Quantitative Structure-Activity Relationship Receptors, Cytoplasmic and Nuclear Toxicity Tests
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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 36040137
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...
Binding Sites Ligands Machine Learning Receptors, Cytoplasmic and Nuclear
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Profiling prediction of nuclear receptor modulators with multi-task deep learning methods: toward the virtual screening.

Briefings in bioinformatics 35998896
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...
B-Cell Activation Factor Receptor Calcitonin Receptor-Like Protein Cytokine Receptor gp130 Deep Learning Histamine H2 Antagonists Ligands Neurokinin-1 Receptor Antagonists Proto-Oncogene Proteins c-met Receptor-Like Protein Tyrosine Phosphatases, Class 2 Receptor, Metabotropic Glutamate 5 Receptors, Artificial Receptors, Aryl Hydrocarbon Receptors, Calcitriol Receptors, Cytoplasmic and Nuclear Receptors, Gastrointestinal Hormone Receptors, Muscarinic Receptors, Polymeric Immunoglobulin
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