virtual-screening

You can automate virtual screening by integrating tools like AutoDock Vina, SMINA, and GNINA to handle receptor and ligand preparation, binding site detection, and docking. This workflow identifies potential binders for a given protein target without requiring extensive experimental testing.

Yes, virtual screening workflows support screening ultralarge libraries like ZINC22 and Enamine REAL. The integrated pipeline handles ligand preparation using meeko and OpenBabel, allowing large-scale docking against your target protein using GNINA or SMINA.

Rigid docking treats the receptor as a static structure, while flexible docking allows specific receptor side chains to move. This virtual screening workflow offers both options, alongside cross-docking and self-docking scenarios, to accurately predict protein-ligand binding affinities.

Yes, RDKit, meeko, and OpenBabel are required dependencies for ligand preparation. They convert and optimize chemical structures into the proper formats needed for docking tools like SMINA, GNINA, and AutoDock Vina to process accurately.

Binding site detection is handled using P2Rank within the virtual screening workflow. If you provide a receptor PDB file, the pipeline can automatically identify potential binding pockets before running the docking simulations with AutoDock Vina or GNINA.

Yes, GNINA supports convolutional neural network (CNN) scoring for rescoring docking poses. You can execute this by passing the CNN scoring option, such as `--cnn_scoring rescore`, during your virtual screening workflow to refine binding affinity predictions.