Name

Data-driven deep learning docking

Software

Schrodinger/In-house Deep Learning

System Preparation Parameters

Assumed pH 4.5
Tautomers considered
Gasteiger charges

System Preparation Method

Proteins and Ligands were prepared by Maestro's prepwizard with default parameters.
Ligand conformational libraries were generated using ConfGen in Schrodinger.

Pose Prediction Parameters

num_conformers=200

Pose Prediction Method

Ligands in the Protein Data Bank were collected based on the FP2 score to targets. The ligand conformers of each target were aligned to templates. The in-house deep learning algorithms were utilized to rank those poses.

Answer 1

No

Answer 2

Yes

Name

Deep-Learning-Package-DC

Software

AG/DG/TDL-BP/Schrodinger

Parameters

Use AG, DG and/or TDL-BP with default parameters
Use PDBBind as the training data for these models

Method

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

Answer 1

No

Answer 2

Yes