For the first four metrics, the identity matrix is then subjected to clustering. Two options are available: Kmedoids and Affinity Propagation . These methods were selected because they output a cluster member as the cluster center, which represents a real ligand pose. For the K-medoids algorithm, the ideal AutoDock4 Semi-empirical Semi-empirical scoring function from AutoDock4 GNINA-Affinity Empirical Empirical scoring function bundled in GNINA AAScore Empirical Amino-acid specific empirical scoring LinF9 Empirical Improved scoring based on 9 empirical terms CHEMPLP Empirical Expanded PLP with GOLD terms for metals and torsions PLP Empirical Piecewise linear potential (4 terms) Vinardo Empirical Improved empirical scoring based on Vina KORP-PL Knowledge-based Coarse-grained knowledge-based scoring Convex-PL R Knowledge-based Scoring function incorporating entropic terms CNN-Score Machine-learning CNN pose scoring model bundled in GNINA CNN-Affinity Machine-learning CNN binding affinity prediction bundled in GNINA RF-Score-VS Machine-learning Random Forest-based scoring function SCORCH Machine-learning Consensus ML scoring function RTMScore Machine-learning Graph transformer-based scoring function PLEC Machine-learning Protein-ligand fingerprint binding affinity prediction NNScore Machine-learning Neural network-based affinity prediction number of clusters is determined by using the silhouette score and an elbow-finding library . Only the cluster center poses are selected for further processing. Finally, any one of the 16 scoring functions supported by DockM8 can be used to select docking poses. In this case, the best scoring pose for each ligand is used for further calculations.