Expansive discovery of chemically diverse structured macrocyclic oligoamides

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Structural Biology

Background:

• Small macrocycles are potent natural products but lack systematic generation methods.
• Current limitations hinder the exploration of diverse macrocyclic chemical space for drug development.

Purpose of the Study:

• To develop a computational method for systematically designing novel ordered macrocycles.
• To chemically synthesize and structurally validate computationally designed macrocycles.
• To demonstrate the therapeutic potential of designed macrocycles as selective inhibitors.

Main Methods:

• Utilized a computational approach to identify ordered macrocycles across various amino acid chemistries.
• Predicted 14.9 million unique macrocyclic structures from over 42,000 monomer combinations.
• Synthesized 18 predicted macrocycles and confirmed their structures using X-ray and NMR spectroscopy.

Main Results:

• Fifteen of the 18 synthesized macrocycles closely matched their computational design models.
• Demonstrated the feasibility of synthesizing computationally designed macrocycles with high structural fidelity.
• Developed selective inhibitors for three therapeutically relevant protein targets using designed macrocycles.

Conclusions:

• The developed computational method enables systematic generation of diverse, drug-like macrocycles.
• This approach significantly expands the accessible chemical space for macrocycle-based drug design.
• The findings are expected to enhance structure-based drug design and accelerate therapeutic development.