A Definition of "Multitargeticity": Identifying Potential Multitarget and Selective Ligands Through a Vector Analysis

Area of Science:

• Medicinal Chemistry
• Computational Drug Design
• Pharmacology

Background:

• Multitarget drugs are crucial for managing complex diseases.
• Defining and identifying multitarget drugs remains a significant challenge in drug discovery.

Purpose of the Study:

• To introduce a novel vector analysis for quantifying "multitargeticity."
• To develop and validate two new parameters, multitarget indexes 1 and 2, for discriminating multitarget drugs.

Main Methods:

• Development of a vector analysis approach defining ligand "order" and "force."
• Creation of two multitarget indexes (1 and 2) based on the vector analysis.
• Validation using experimental, docking, and database training sets for in silico and experimental data.

Main Results:

• The proposed indexes effectively discriminate multitarget drugs from selective ligands.
• Successful identification of actual multitarget compounds across various training sets.
• Application to a multiple sclerosis drug target library identified 10 potential multitarget drug leads.

Conclusions:

• The developed multitarget indexes represent a significant advancement in defining and identifying multitarget drugs.
• This methodology offers a robust tool for drug design and discovery, particularly for complex diseases.
• The findings pave the way for more effective multitarget drug development strategies.