Consensus scoring scheme
CODASS4: COnsensus Docking And Similarity Search
Enamine REAL
Enamine_3M
Vina-GPU+, SILCS-MC, DeepDock, DiffDock, SAIYAN*
PoseMatch
Visual analysis
Autodock-GPU
Purchase & test
39m
50k�hit�similars
Initial SBVS-based search to predict binding poses
Additional docking to complete our consensus pose prediction scheme
Our open-source pose similarity calculator
COMPRISING:
SILCS-MC
P3-Score
RFScore-VSv2
SCORCH 2.0 (inc. RFRanker)
DeepDock
TargetSF (target-specific SF trained using the 895 known ligands)
CRYSTAL LIGAND CONFORMATION (PDB 8GCY) + 895 ligands with binding poses predicted via Consensus Docking and the reliability estimator of SCORCH
2D: FP2 Tanimoto
3D: USRCAT, SILCS-Pharm, OpenFEPOPS, Autodock-SS
195m conformers
GWOVina
REAL46M_�filtered
3m
Top 5k virtual hits or “seeds”
36b compounds
50k virtual hits
Houston DR, Walkinshaw MD. “Consensus docking: improving the reliability of docking in a virtual screening context”. J Chem Inf Model. 2013
Houston DR, et al. “CODASS: A New Process for Ligand Discovery In Silico”, Conference: COMPUTATIONAL CHEMICAL BIOLOGY: probing biology with in silico tools. The University of Manchester. 2012
Our multiconformer “lead-like” subset of Enamine REAL
Maximally diverse subset of REAL46M
Fast LBVS-based search
*Note SAIYAN will not be used unless and until it demonstrates good performance against benchmark test datasets such as PDBbind 2021 and DUDE-Z:
Tools developed in house are shown in yellow in the previous slide
Target-specific tools trained via the addition of the 895 known ligands are underlined