buildAGL-Score Input

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local_libraryReferences

1 Duc D. Nguyen and Guo-Wei Wei, AGL-Score: Algebraic graph learning score for protein-ligand binding scoring, ranking, docking, and screening (2019) 2 Duc D. Nguyen and Guo-Wei Wei, Algebraic graph learning of protein-ligand binding affinity (2018) Read

local_libraryTraining Datasets

Below are the datasets used to train AGL-Score for docking and screening power assessments

  • Docking Power Dataset: Poses folder contains the structures for training data, INDEX folder consists of labels of the corresponding structures in Poses folder
  • Screening Power Dataset: Poses folder contains the computer-generated structures for training data and test data, INDEX folder consists of predicted energies of the training data in the Poses folder

perm_contact_calendarContact Information

Contact us : weig at msu.edu
Webmaster : ddnguyen at msu.edu

layersAGL-Score's Flow Chart

flowchart

extensionUser guide

AGL-Score is graph theory based scoring function combined with an advance machine learning algorithm for protein-ligand binding scoring, ranking, docking, and screening

Your INPUT includes
  • Protein in PDB format
  • Ligand in SDF or MOL2 format

Your OUTPUT includes
  • Predicted binding energy in kcal/mol