Macro-Equi-Diff (MED): Scaffold-based Macrocycles Generation Using Equivariant Diffusion

Abstract Macrocyclic compounds are essential in drug discovery as they can modulate protein–protein interactions and enhance selectivity. Their structural complexity enables access to molecular diversity beyond traditional small molecules; however, designing feasible macrocycles remains a challenging task. Current computational methods often fail to generate macrocycles with proper drug-like properties. Here, we present Macro-Equi-Diff (MED), a deep learning framework that combines transformer-based site identification with an E(3)-equivariant Diffusion Model (EDM) for linker creation, and a fragment–linker attachment module. MED transforms acyclic molecules into structurally consistent macrocycles. MED was tested on the ZINC dataset, achieving high validity (93.92%), uniqueness (99.94%), macrocyclization (99.92%), and linker novelty (82.81%). MED improves upon previous methods that lack a macrocyclic geometry context. As a case study, MED was used to macrocyclize four acyclic drugs targeting the JAK2 protein. The generated macrocycles exhibited favourable molecular descriptors and strong binding affinities, establishing MED as a reliable method for expanding the macrocyclic chemical space.