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KeyDocking, the most advanced 3D ligand feature-based docking method, is a novel ligand-based docking technique which combines ligand feature pharmacophore screening with flexible protein docking. The key to KeyDocking is in the discovery of pharmacophore features on the ligand that correspond to functionally important regions of the protein. This functional correspondence can be predicted a priori through the use of ligand-based pharmacophore modeling. In cases where no pharmacophore is available, KeyDocking can be used to generate pharmacophore features that contain specific pharmacophore groups associated with important regions of the protein. The ligand pharmacophores, and protein pharmacophores are then used to generate a set of steric and electrostatic constraints that are incorporated into the docking process. The spatial positions of the pharmacophores are optimized at the same time as the ligand poses are searched for an optimal conformation. Ligand features that match the pharmacophore constraints are scored for their fit to the protein pharmacophores, and then ranked based on their score.
KeyDocking provides a flexible scheme for protein-ligand docking by allowing the position of pharmacophore features to vary along with the ligand. This feature is particularly useful in screening large libraries of compounds to identify active conformation ensembles or to generate multiple pharmacophore hypotheses. A similarity measure is used to determine the best fitting poses within an ensemble. The similarity metric is the epsilon-delta similarity score which measures the deviation of a ligand feature from an ideal pharmacophore. The ligand pharmacophores are represented as a dictionary of user-defined features that are defined by the pharmacophore model. The pharmacophore is defined by a core set of pharmacophore features, while the key ligand features may differ for each ligand pose in the ensemble. The ligand key features are defined by the user-specified pharmacophore model. The pharmacophore features are defined in two ways. Pharmacophore features are encoded as a vector of pharmacophore groups (as opposed to individual pharmacophore points). The pharmacophore groups are defined by pharmacophore features that are spatially close to one another (i.e., share the same pharmacophore groups). In this encoding, the pharmacophore groups form the main body of the pharmacophore feature, while the pharmacophore points that define the pharmacophore groups are ancillary features that can be located at a distance

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