Computational Prediction Of Glp-1 Antagonist Ligand Interaction

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This project developed a fully student-built, open-source computational pipeline to investigate GLP-1 agonistreceptor interactions. Ligand and receptor sequences were curated from UniProt and modeled into three-dimensional structures with AlphaFold.

Abstract The large amount of data that has been collected so far for G protein-coupled receptors requires machine learning (ML) approaches to fully exploit its potential. Our previous ML model based on gradient boosting used for prediction of drug affinity and selectivity for a receptor subtype was compared with explicit information on ligand-receptor interactions from induced-fit docking ...

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Computational Prediction Of Glp-1 Antagonist Ligand Interaction

Results for GLP-1R compounds similar to the binding site 1 ligand. The lower binding energy (Autodock VINA score), the better fitness between the ligand and the type of the receptor binding site.

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