FastGrow: on-the-fly growing and its application to DYRK1A.

Area of Science:

• Computational chemistry and drug discovery.
• Structural biology and molecular modeling.

Background:

• Fragment-based drug design (FBDD) is a key strategy in drug discovery.
• Growing initial fragment hits into potent ligands remains a significant challenge.
• Efficient computational tools are needed to support FBDD workflows.

Purpose of the Study:

• To introduce FastGrow, a novel computational application for accelerating ligand development in FBDD.
• To present FastGrow's capabilities in rapid fragment growing and structure-based modeling.
• To validate FastGrow's performance against existing docking software and in a kinase case study.

Main Methods:

• Development of FastGrow based on a shape search algorithm.
• Integration of pharmacophoric interaction description, ensemble flexibility, and geometry optimization.
• Evaluation using crystallographic data from fragment growing scenarios.
• Comparative analysis with established docking software.
• Case study application on DYRK1A kinase inhibitors.

Main Results:

• FastGrow achieves high-speed fragment growing (milliseconds per fragment).
• The application integrates multiple features for comprehensive structure-based modeling.
• Performance was validated against crystallographic data and found competitive with docking software.
• FastGrow successfully identified active fragments in a DYRK1A kinase case study.

Conclusions:

• FastGrow is an effective and rapid tool for fragment growing in drug design.
• Its comprehensive features make it a valuable addition to structure-based modeling toolkits.
• The software is publicly available as a web server and part of the SeeSAR 3D package.