Target Pocket Library

In the process of new drug development, finding the correct target pocket structure can greatly improve the development efficiency. This module is designed to help users quickly define pocket structures as the basis for subsequent molecular screening, design, optimization, and evaluation.

The platform uses deep learning algorithms to automatically predict one to five most competitive pocket structures for each protein and form a 3D visual selection interface.

Besides automated pocket prediction, the pockets can be established by defining a set of key amino acid residues manually at first. Then, a set of self-developed intelligent algorithms automatically completes the pocket structures.

Furthermore, the module creates an associated profile (called Target Pocket) for each pocket that can be viewed and recalled at any time. In addition, more functions, such as defining the pocket structure precisely to each amino acid site, etc., will be added to the module in the future to suit the needs of different application scenarios.

Create a Target Pocket

This GIF shows the steps to create a target pocket in MolProphet:

TIP

When creating target pockets, users have the option to upload local files for instant processing or search for standard PDB files using PDB IDs sourced from the official PDB Web site (https://www.rcsb.org/).