Data-driven deep learning docking/Schrodinger
Schrodinger/In-house Deep Learning
Assumed pH 5.0
Tautomers considered
Gasteiger charges
Proteins and Ligands were prepared by Maestro's prepwizard with default parameters.
Ligand conformational libraries were generated using ConfGen in Schrodinger.
num_conformers=200
Poses were ranked by in-house deep learning score. Pose with the highest score were chosen.
No
No
Deep Learning Package
AG/DG/TDL-BP/Schrodinger
Use AG, DG-GL and/or TDL-BP with default parameters
Use PDBBind as the training data for these models
The features generated by Algebraic Graph, Differential geometry, and Algebraic topology scores are utilized in in-house design neural network models to predict the binding free energy
No
Yes