Our goal with this competition is to evaluate our released tools. Namely, gnina (https://github.com/gnina) and pharmit (http://pharmit.csb.pitt.edu).
We will perform hierarchical, "blind" high-throughput docking on the Enamine Real Database. We will iteratively dock batches of ligands from Enamine Real to a single target structure using gnina and select subsequent batches of ligands based on the resulting docking scores. New batches of Enamine Real compounds will either be selected by similarity to the best-scoring docked ligands or by a simple ML model trained to predict docking scores from ligand fingerprints. The single target structure used for docking will be selected from among the several available crystal structures based on druggability scores.
Pharmit will be used to perform pharmacophore screens of purchasable compounds. Pharmacophores will be constructed in a mostly manual process through inspection of known binders and docked fragments. We will screen all the commercial libraries in Pharmit (ZINC, MolPort, MCULE, Chemspace, LabNetwork).
Hit compounds will be ranked using the default deep learning scoring function of gnina. Multiple conformations of the target structure will be used for final pose optimization and ranking. We will merge the ranked lists from high-throughput docking and pharmacophore screening, but ensure that compounds from both are included in our submission.