Design of a three-dimensional multitarget activity landscape

Area of Science:

• Medicinal Chemistry
• Cheminformatics
• Computational Drug Discovery

Background:

• Representing compound activity across multiple targets is complex, especially beyond 3-4 targets.
• Conventional activity landscape views struggle to capture the intricate nature of multitarget activity spaces.
• Previous multitarget landscape methods often rely on molecular networks or pairwise similarity plots.

Purpose of the Study:

• To develop an advanced 3D multitarget activity landscape representation.
• To overcome limitations of conventional methods in visualizing complex activity spaces.
• To facilitate interactive analysis and identification of compounds with specific selectivity profiles.

Main Methods:

• Utilized principles of radial coordinate visualization for a 3D landscape.
• Integrated circular representations of multitarget activity and chemical reference space.
• Developed three canonical views (activity, chemical, combined) for landscape interpretation.

Main Results:

• Introduced a spherical visualization for projecting compound sets.
• Enabled interactive analysis of multitarget activity and chemical space.
• Demonstrated extraction of compounds with well-defined target selectivity and structure-activity relationships.
• Identified characteristic spatial arrangements and activity patterns for selective compounds.

Conclusions:

• The 3D multitarget activity landscape offers a powerful tool for complex drug discovery scenarios.
• This visualization method enhances the identification of compounds with desired selectivity profiles.
• The approach provides novel insights into structure-activity relationships in multitarget drug design.