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[2602.14977] MacroGuide: Topological Guidance for Macrocycle Generation
Summary
The paper introduces MacroGuide, a novel diffusion guidance mechanism that enhances the generation of macrocycles in molecular modeling, achieving significant improvements in generation rates and quality metrics.
Why It Matters
Macrocycles are crucial in drug discovery due to their unique properties, yet their generation has been limited by existing modeling techniques. MacroGuide addresses these challenges, potentially accelerating the development of new therapeutics and improving the efficiency of generative models in chemistry.
Key Takeaways
- MacroGuide utilizes Persistent Homology to improve macrocycle generation.
- It increases generation rates from 1% to 99%, demonstrating significant effectiveness.
- The method maintains or exceeds quality metrics like chemical validity and diversity.
- MacroGuide can be applied in both unconditional and conditional settings.
- This advancement could enhance drug discovery processes.
Computer Science > Machine Learning arXiv:2602.14977 (cs) [Submitted on 16 Feb 2026] Title:MacroGuide: Topological Guidance for Macrocycle Generation Authors:Alicja Maksymiuk, Alexandre Duplessis, Michael Bronstein, Alexander Tong, Fernanda Duarte, İsmail İlkan Ceylan View a PDF of the paper titled MacroGuide: Topological Guidance for Macrocycle Generation, by Alicja Maksymiuk and 5 other authors View PDF HTML (experimental) Abstract:Macrocycles are ring-shaped molecules that offer a promising alternative to small-molecule drugs due to their enhanced selectivity and binding affinity against difficult targets. Despite their chemical value, they remain underexplored in generative modeling, likely owing to their scarcity in public datasets and the challenges of enforcing topological constraints in standard deep generative models. We introduce MacroGuide: Topological Guidance for Macrocycle Generation, a diffusion guidance mechanism that uses Persistent Homology to steer the sampling of pretrained molecular generative models toward the generation of macrocycles, in both unconditional and conditional (protein pocket) settings. At each denoising step, MacroGuide constructs a Vietoris-Rips complex from atomic positions and promotes ring formation by optimizing persistent homology features. Empirically, applying MacroGuide to pretrained diffusion models increases macrocycle generation rates from 1% to 99%, while matching or exceeding state-of-the-art performance on key quality met...
