Current State-of-the-art for Virtual Screening and Docking Methods - New Developments in Medicinal Chemistry - page 95

EAISFD is a computer assisted de novo drug design method, which combines the
design engine EA-inventor with a scoring function featuring the molecular
program Surflex-Dock. The method uses tagged fragments. These are preserved
substructures in EA-Inventor. They are also used for base fragment matching in
Surflex-Dock (constructing ligand structures under specific binding motifs). A
target score mechanism is adopted that allows the method to deliver a diverse set
of desired structures. The method can be used to design novel ligand scaffolds
(lead generation) or to optimize attachments on a fixed scaffold (lead
optimization) [506].
PythDock is a heuristic docking program that uses Phython programming
language with a simple scoring function and population based search engine. The
scoring function considers electrostatic and dispersion/repulsion terms. The search
engine utilizes a particle swarm optimization algorithm. A grid potential map is
generated using the shape information of a bound ligand within the active site.
The searching area is more relevant to the ligand binding [507].
CAPRI implements a comprehensive suite of docking algorithms which were
incorporated into a dynamic docking protocol consisting of four major stages. The
first stage is a biological and bioinformatics research aimed at predicting the
binding site residues. This is to define distance constraints between interface
atoms and to analyze the flexibility of molecules. The second stage is rigid or
flexible docking. This is performed by the PatchDock or FlexDock method, which
utilizes the information gathered in the previous step. In the third stage symmetric
complexes are predicted by the SymmDock method. In the fourth stage there is
flexible refinement and reranking of the rigid docking solution candidates. This is
performed by Fiber Dock clustering and filtering the results based on energy
funnels [508].
VoteDock uses a consensus algorithm attempt to combine different docking
approaches into a single and powerful prediction method. A set of representative
conformations are selected from each docking algorithm to efficiently inspect
different guided search algorithms for correct conformation of a protein-ligand
complex [509].