DG-GL: Differential geometry based geometric data analysis (DG-GDA) of molecular datasets

DG-GL Input

Protein file

(in pdb format)

Select file Change

Ligand file

(in sdf or mol2 format)

Select file Change

Training data

v2016

Ligand file

(in sdf or mol2 format)

Select file Change

Ligand file

(in sdf or mol2 format)

Select file Change

This is optional. A message will be sent to your emal when your job is done.

References

1 Duc D. Nguyen and Guo-Wei Wei, DG-GL: Differential geometry based geometric learning of molecular datasets, International Journal for Numerical Methods in Biomedical Engineering, 35 (3), e3179 (2019) Read

Contact Information

Webmaster : ddnguyen at math.msu.edu

DG-GL Input

References

Contact Information

DG-GL's Flow Chart

User guide

RI-Score is machine-learning based approach to predict a binding energy (in kcal/mol) of a complex

Your INPUT includes

Protein in PDB format

Ligand in SDF or MOL2 format

Notice that: your protein and ligand have already formed a complex

Your OUTPUT includes

Predicted binding energy in kcal/mol

buildDG-GL Input

local_libraryReferences

perm_contact_calendarContact Information

layersDG-GL's Flow Chart

extensionUser guide

RI-Score is machine-learning based approach to predict a binding energy (in kcal/mol) of a complex

Your INPUT includes Protein in PDB format Ligand in SDF or MOL2 format Notice that: your protein and ligand have already formed a complex

Your OUTPUT includes Predicted binding energy in kcal/mol

DG-GL Input

References

Contact Information

DG-GL's Flow Chart

User guide

Your INPUT includes

Protein in PDB format

Ligand in SDF or MOL2 format

Notice that: your protein and ligand have already formed a complex

Your OUTPUT includes

Predicted binding energy in kcal/mol