idTarget

idTarget identifies potential biomolecular targets of small chemical molecules by docking them against protein structures from the Protein Data Bank (PDB) to elucidate molecular mechanisms of action.

Key Features:

• Divide-and-Conquer Docking Approach: Utilizes a divide-and-conquer docking strategy that adaptively constructs small overlapping grids to constrain the search space for docking simulations.
• Robust Scoring Functions: Applies scoring functions derived from robust regression analysis and quantum chemical charge models to evaluate binding affinities between small molecules and proteins.
• Comprehensive PDB Screening: Screens candidate ligands against nearly all protein structures available in the Protein Data Bank (PDB) to provide broad coverage of potential targets.
• Affinity Profiles and Confidence Assessment: Generates affinity profiles for predicted protein targets and assigns confidence levels to prioritize likely interactions.

Scientific Applications:

• Drug discovery: Prioritizes protein targets for small molecules to support lead identification and optimization.
• Drug repurposing: Predicts alternative protein targets of drugs or drug-like compounds to suggest new therapeutic uses.
• Off-target and side-effect analysis: Identifies known or potential off-target interactions to aid understanding of adverse effects.
• Target nomination for experimental follow-up: Suggests novel protein targets for biochemical or cellular validation studies.

Methodology:

Performs divide-and-conquer docking using adaptively constructed small overlapping grids, applies scoring functions derived from robust regression and quantum chemical charge models, screens against nearly all PDB structures, and generates affinity profiles to assign confidence to predictions.