FastGrow

FastGrow grows fragment hits into optimized ligand candidates using a shape-based algorithm to accelerate fragment-based drug design.

Key Features:

• Shape-Based Algorithm: A shape search algorithm identifies and grows fragment hits into potential ligands.
• Rapid Processing Speed: Evaluates fragments in a few milliseconds per fragment.
• Pharmacophoric Interaction Description: Implements pharmacophore-like constraints to preserve essential protein–ligand interactions during growth.
• Ensemble Flexibility: Incorporates ensemble approaches to account for conformational flexibility in protein–ligand complexes.
• Geometry Optimization Using JAMDA: Performs geometry optimization of growing fragments via the Joint Atomic-Molecular Dynamics Algorithm (JAMDA).

Scientific Applications:

• Crystallographic Fragment-Growing Evaluation: Validated on fragment-growing scenarios derived from crystallographic data.
• Benchmarking Against Docking Software: Benchmarked against established docking software to assess performance in structure-based modeling.
• DYRK1A Kinase Case Study: Applied to DYRK1A kinase data to identify active fragments and new chemotypes.

Methodology:

Uses a shape search algorithm with pharmacophoric constraints, ensemble-based modeling for flexibility, and geometry optimization via JAMDA.