EDock-ML

(Ensemble Docking With Machine Learning)

Paper describing the method.

Our Models

Input: Affinity scores for the protein's selected structures.

Machine Learning

Write in previously docked affinity scores for the structure of a specific protein and run machine learning models to determine whether the drug candidate / ligand is likely to be in the 'active' or 'decoy' group.


Input: .pdbqt file

Machine Learning and Docking

Upload a .pdbqt file for a ligand and select the protein that it should dock to. Then, the program will dock the protein using Vina and run machine learning models to determine whether the drug candidate / ligand is likely to be in the 'active' or 'decoy' group.


Input: .pdbqt file

Docking

Upload a .pdbqt file for a ligand and the protein will dock it to multiple structures for a given protein and return affinity scores. After docking is complete, there will be an option to run the machine learning models.