Computational Macrocyclization: From de novo Macrocycle Generation to Binding Affinity Estimation.

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Drug Discovery

Background:

• Macrocycles are increasingly important in drug discovery but are challenging to synthesize.
• Predictive computational tools are needed to guide the design and synthesis of macrocyclic drugs.
• Current methods lack robust ways to estimate binding affinity and suggest macrocyclization strategies.

Purpose of the Study:

• To establish a computational workflow for predicting the impact of macrocyclization on binding affinity.
• To develop and validate a method for suggesting novel macrocyclization strategies based on binding modes.
• To create a practical tool for medicinal chemists to design and prioritize macrocyclic drug candidates.

Main Methods:

• Utilized computational chemistry methods to calculate binding affinity changes upon macrocyclization.
• Developed and applied a workflow integrating binding mode information with macrocyclization predictions.
• Validated the computational approach across five diverse pharmaceutical targets.
• Employed LigMac, a de novo macrocyclization search tool, to design candidate macrocycles.

Main Results:

• Demonstrated robust and accurate calculation of macrocyclization's effect on binding for multiple targets.
• Successfully applied the workflow to macrocycles designed by the LigMac tool.
• Showcased the practical utility of the developed computational protocol.
• Achieved reliable prediction of binding affinity for designed macrocycles.

Conclusions:

• A robust computational protocol for designing and prioritizing macrocycles in drug discovery has been established.
• The workflow enables accurate prediction of binding affinity, significantly aiding medicinal chemistry efforts.
• This approach facilitates the rational design of macrocyclic drugs, reducing synthetic challenges and improving efficiency.