ComTarget: Small-Molecule Target Prediction with Combinatorial Modeling.

Area of Science:

• Computational chemistry
• Drug discovery
• Bioinformatics

Background:

• Identifying bioactive compound targets is essential for understanding drug mechanisms and advancing drug development.
• Existing methods may not fully capture crucial 3D structural information for target identification.

Purpose of the Study:

• To introduce ComTarget, a computational tool for predicting protein targets of small molecules.
• To integrate 3D molecular shape similarity with reverse docking for enhanced target prediction accuracy.

Main Methods:

• ComTarget utilizes combined 3D descriptors (C3DD) for molecular shape similarity analysis.
• A library of 4429 unique protein targets from 26,272 PDB complexes was used for screening.
• Reverse docking was employed in conjunction with 3D shape analysis.

Main Results:

• The C3DD method effectively enriched active ligands by capturing 3D shape data, outperforming 2D fingerprint methods.
• ComTarget identified known and potential targets for FDA-approved drugs and natural products, aligning with pharmacological mechanisms.
• The tool demonstrated a balance between shape sensitivity and computational efficiency, suitable for rapid pre-screening.

Conclusions:

• ComTarget is a flexible, locally installable program for polypharmacology studies, drug repurposing, and adverse reaction prediction.
• The tool's ability to identify targets based on 3D shape enhances its utility in drug discovery workflows.
• ComTarget provides valuable insights into compound-target interactions and potential off-target effects.