AIMC Topic: 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 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...
Deep Learning Receptors, Calcitriol Receptors, Aryl Hydrocarbon Receptors, Muscarinic Ligands Receptors, Cytoplasmic and Nuclear Receptor, Metabotropic Glutamate 5 Receptors, Artificial Proto-Oncogene Proteins c-met B-Cell Activation Factor Receptor Calcitonin Receptor-Like Protein Cytokine Receptor gp130 Histamine H2 Antagonists Neurokinin-1 Receptor Antagonists Receptor-Like Protein Tyrosine Phosphatases, Class 2 Receptors, Gastrointestinal Hormone Receptors, Polymeric Immunoglobulin
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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...
Humans Machine Learning Animals Computational Biology Receptors, Cytoplasmic and Nuclear
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