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Receptors, G-Protein-Coupled

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Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition.

Methods (San Diego, Calif.)
In the last decade, machine learning and artificial intelligence applications have received a significant boost in performance and attention in both academic research and industry. The success behind most of the recent state-of-the-art methods can be...

A Machine Learning Approach for Drug-target Interaction Prediction using Wrapper Feature Selection and Class Balancing.

Molecular informatics
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 to Identify Flexibility Signatures of Class A GPCR Inhibition.

Biomolecules
We show that machine learning can pinpoint features distinguishing inactive from active states in proteins, in particular identifying key ligand binding site flexibility transitions in GPCRs that are triggered by biologically active ligands. Our anal...

Integrated structural modeling and super-resolution imaging resolve GPCR oligomers.

Progress in molecular biology and translational science
Formation of G protein-coupled receptors (GPCRs) dimers and higher order oligomers represents a key mechanism in pleiotropic signaling, yet how individual protomers function within oligomers remains poorly understood. For the Class A/rhodopsin subfam...

Development of New Methods Needs Proper Evaluation-Benchmarking Sets for Machine Learning Experiments for Class A GPCRs.

Journal of chemical information and modeling
New computational approaches for virtual screening applications are constantly being developed. However, before a particular tool is used to search for new active compounds, its effectiveness in the type of task must be examined. In this study, we co...

Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure.

Genomics
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...

Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [S]GTPγS Binding Assays.

ACS chemical neuroscience
G-protein-coupled receptors (GPCRs), also known as 7-transmembrane receptors, are the single largest class of drug targets. Consequently, a large amount of preclinical assays having GPCRs as molecular targets has been released to public sources like ...

A Machine Learning Approach for the Discovery of Ligand-Specific Functional Mechanisms of GPCRs.

Molecules (Basel, Switzerland)
G protein-coupled receptors (GPCRs) play a key role in many cellular signaling mechanisms, and must select among multiple coupling possibilities in a ligand-specific manner in order to carry out a myriad of functions in diverse cellular contexts. Muc...

Exploring the Potential of Spherical Harmonics and PCVM for Compounds Activity Prediction.

International journal of molecular sciences
Biologically active chemical compounds may provide remedies for several diseases. Meanwhile, Machine Learning techniques applied to Drug Discovery, which are cheaper and faster than wet-lab experiments, have the capability to more effectively identif...

Applications of machine learning in GPCR bioactive ligand discovery.

Current opinion in structural biology
GPCRs constitute the largest druggable family having targets for 475 Food and Drug Administration (FDA) approved drugs. As GPCRs are of great interest to pharmaceutical industry, enormous efforts are being expended to find relevant and potent GPCR li...