Docking Pose Processing

Clustering

• ClusterX: ClusterX is a deep clustering framework for learning molecular representations of protein-ligand complexes and accurately clustering ligands, designed to assist computational medicinal chemists in making visual decisions.
  

  

Interaction Filtering

• BINANA: BINANA analyzes the geometries of predicted ligand poses to identify molecular interactions that contribute to binding, also featuring a web-browser application for visualizing these interactions.
  

  

• LigGrep: LigGrep is a program for identifying docked poses that participate in specified receptor/ligand interactions, accepting a protein receptor file, docked-compound files, and user-specified filters as input.

  

• vsFilt: A Tool to Improve Virtual Screening by Structural Filtration of Docking Poses (detect various types of interactions that are known to be involved in the molecular recognition, including hydrogen and halogen bonds, ionic interactions, hydrophobic contacts, pi-stacking, and cation-pi interactions) (online).

  

  

Interaction recovery

• plif_validity: Assessing interaction recovery of predicted protein-ligand poses
  

  

Ligand Stability

• MDFit: protein–ligand compatibility, including stability of different ligand-pocket interactions and other useful metrics that enable easy rank-ordering of the ligand library for pocket optimization
  

  

ML Pose Selection

• Classy_Pose: ClassyPose: A Machine‐Learning Classification Model for Ligand Pose Selection Applied to Virtual Screening in Drug Discovery
  

  

Metadynamics

• OpenBPMD: evaluating ligand pose stability using metadynamics
  

  

Minimization

• DeepRMSD-Vina_Optimisation: This algorithm is based on deep learning and a classical scoring function (Vina score) and is designed to optimize ligand conformations.
  

• Energy minimization post-processing used in PoseBusters: PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences
  

  

• MMO: Molecular Mechanics in OCaml: An ANI‐2 enabled open‐source protocol to estimate ligand strain after docking
  

  

• PyRosettta:
  

  

• Vina_pose_Optimization: optimize positions of ligand polar hydrogens in docking pose
  

• SiteInterlock: based upon the hypothesis that interfacial rigidification of the protein-ligand interface is an important characteristic that can detect the native ligand binding mode
  

  

Quality Assessment

• DockQ: DockQ is now also able to score interfaces between proteins, nucleic acids, and small molecules.
  

  

• PECAN: Implements convolutional neural network approaches for improving virtual high-throughput screening, using 3D atomic representation as input data.
  

  

• PLIF_validity: Assessing interaction recovery of predicted protein-ligand poses
  

  

• PoseBench: comprehensive benchmark for practical protein-ligand docking
  

  

• PoseBusters: Plausibility checks for generated molecule poses.
  

  

• PoseCheck:
  

  

RMSD calculation

• pyDockRMSD: DockRMSD is an open-source program that identifies the minimum symmetry-corrected RMSD for docked poses without losing computational efficiency, useful for ligand molecules with complex structural symmetry.
  

  

• rmsd: Calculate Root-mean-square deviation (RMSD) of Two Molecules Using Rotation
  

  

• spyRMSD: Python tool for symmetry-corrected RMSD calculations.