Multiple initial conformations re-scored by Convex-PL with a regression-based correction by ligand flexibility 2.
AutoDock Vina with in-house modifications (Convex-PL as a scoring function), RDKit 2018, SciPy, PyMOL 1.8.4, unreleased version of Convex-PL scoring function
1000 conformations for each molecule in RDKit with rings clustered at 0.15A
Ligand conformations were generated in RDKit with similarity-based distance constraints, cycles were clusterized with hierarchical clustering. First 150 clusters were selected for docking.
Vina exhaustiveness 10, Convex PL as a scoring function, number of poses 350-450, Convex-PL rescoring with typization system 14, 0.5A clusterization, Convex-PL distance cutoff at 10A for re-scoring and 10.A + internal ligand clashes for docking.
We ran docking experiments with all available conformations of each ligand except BACE_20, and did the rescoring with regression-based version of Convex-PL.
No
Yes
Multiple initial conformations re-scored with Convex-PL with a regression-based correction by ligand flexibility 2.
AutoDock Vina with in-house modifications (Convex-PL as a scoring function), RDKit 2018, SciPy, PyMOL 1.8.4, unreleased version of Convex-PL scoring function
Vina exhaustiveness 10, Convex PL as a scoring function, number of poses 350-450, Convex-PL rescoring with typization system 14, 0.5A clusterization, Convex-PL distance cutoff at 10A
Ligand conformations were generated in RDKit with similarity-based distance constraints, cycles were clusterized with hierarchical clustering. First 150 clusters were selected for docking. We then re-scored Vina+Convex-PL docking results with Convex-PL with a regression-based correction by ligand flexibility, clusterized top poses and took the mean score of the ensemble of top-10 poses. For docking, receptors were chosen by ligand cycle similarity and by overall ligand similarity.
No
Yes