AIMC Topic: Receptors, Cannabinoid

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CIRCE: Web-Based Platform for the Prediction of Cannabinoid Receptor Ligands Using Explainable Machine Learning.

Journal of chemical information and modeling Sep 7, 2023
The endocannabinoid system, which includes cannabinoid receptor 1 and 2 subtypes (CBR and CBR, respectively), is responsible for the onset of various pathologies including neurodegeneration, cancer, neuropathic and inflammatory pain, obesity, and inf...
Machine Learning Internet Ligands Receptors, Cannabinoid
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Reliable prediction of cannabinoid receptor 2 ligand by machine learning based on combined fingerprints.

Computers in biology and medicine Nov 30, 2022
Cannabinoid receptors, as part of the family of the G protein-coupled receptors (GPCRs), are involved in various physiological functions. Its subtype cannabinoid receptor subtype 2 (CB2), mainly distributed in the periphery, is a crucial therapeutic ...
Machine Learning Random Forest Ligands Receptors, Cannabinoid
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Machine learning for target discovery in drug development.

Current opinion in chemical biology Nov 14, 2019
The discovery of macromolecular targets for bioactive agents is currently a bottleneck for the informed design of chemical probes and drug leads. Typically, activity profiling against genetically manipulated cell lines or chemical proteomics is pursu...
Humans Machine Learning Computer Simulation Antineoplastic Agents Proteomics Drug Evaluation, Preclinical Sesquiterpenes Molecular Targeted Therapy Transient Receptor Potential Channels Lipoxygenase Naphthoquinones Pentacyclic Triterpenes Receptors, Cannabinoid Sesquiterpenes, Guaiane
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Prediction of Orthosteric and Allosteric Regulations on Cannabinoid Receptors Using Supervised Machine Learning Classifiers.

Molecular pharmaceutics May 3, 2019
Designing highly selective compounds to protein subtypes and developing allosteric modulators targeting them are critical considerations to both drug discovery and mechanism studies for cannabinoid receptors. It is challenging but in demand to have c...
Humans Machine Learning Algorithms Animals Support Vector Machine Receptors, Cannabinoid Allosteric Regulation
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Dissecting celastrol with machine learning to unveil dark pharmacology.

Chemical communications (Cambridge, England) May 30, 2019
By coalescing bespoke machine learning and bioinformatics analyses with cell-based assays, we unveil the pharmacology of celastrol. Celastrol is a direct modulator of the progesterone and cannabinoid receptors, and its effects correlate with the anti...
Humans Machine Learning Computational Biology Systems Biology Progesterone Cell Proliferation Triterpenes Pentacyclic Triterpenes Receptors, Cannabinoid
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Ligand biological activity predictions using fingerprint-based artificial neural networks (FANN-QSAR).

Methods in molecular biology (Clifton, N.J.) Jan 1, 2015
This chapter focuses on the fingerprint-based artificial neural networks QSAR (FANN-QSAR) approach to predict biological activities of structurally diverse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to t...
Neural Networks, Computer Algorithms Protein Binding Computational Biology Models, Molecular Ligands Databases, Chemical Quantitative Structure-Activity Relationship Cannabinoids Receptors, Cannabinoid
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