We used machine learning (ML) techniques to identify specific interactions that correlate with the agonist

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Molecular Dynamics and Machine Learning Study of Adrenaline Dynamics in the Binding Pocket of GPCR.

is supported by the residues located at hotspots located on TMs 5, 6, and 7, as shown by supervised machine learning methods.

By using machine learning, AF2 can accurately predict the 3D structures of GPCRs with atomic-level accuracy

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population Identification of S1PR4 as an immune modulator for favorable prognosis in HNSCC through machine learning Prediction of survival and immunotherapy response by the combined classifier of G protein-coupled

Updates in GPCR Research Activity Map and Transition Pathways of G Protein-Coupled Receptor Revealed by Machine Learning.

Advances in bioinformatics and machine learning will play a pivotal role in addressing these obstacles

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