DC-Flow improves cyclic peptide design for better target binding
DC-Flow: Target-Conditioned Cyclic Peptide Design via Delayed-Coupling Flow Matching
Machine Learning
Summary
Designing cyclic peptides that fit specific targets is tricky because their shape and chemical parts need to work together properly. The authors propose DC-FLOW, a new step-by-step method that first shapes the peptide backbone before adding the chemical details, helping avoid confusion during design. This approach lets the backbone and chemical parts develop together, resulting in peptides that close properly into loops and stick well to targets. Tests show DC-FLOW creates more reliable and diverse cyclic peptides that bind better.
What this means in practice
- •For drug developers: Generate diverse cyclic peptide candidates that reliably bind target proteins with improved structural closure for drug discovery.
- •For biotech engineers: Design cyclic peptides with enhanced interface packing by coordinating backbone shape and residue identities during computational synthesis.
Authors
Yitian Wang, Minjie Cheng, Yaning Cui, Maohua Yang, Dongdong Wang, Hongteng Xu
Abstract
Target-conditioned cyclic peptide design requires coordinating interfacial side chains with macrocyclic closure. Existing end-to-end generators concurrently update geometric and chemical variables from noise, assigning residues to disordered coordinates. Meanwhile, predictor-assisted workflows often get stuck in local traps because they lack feedback in the propose-and-filter stage and ignore 3D structural proposals during sequence search. To overcome the drawbacks of these two paradigms, we propose DC-FLOW, a staged generative framework built around Delayed-Coupling Flow Matching. In principle, DC-FLOW postpones sequence and side-chain activation until a target-conditioned backbone takes shape, resolving structural ambiguity before chemical assignment. Then, it feeds delayed activations back into the backbone, achieving co-evolution of backbone geometry and emerging residue identities by coupling their flows. This mechanism enables more reliable macrocyclic closure and favorable interface packing. For predictor-assisted workflows, DC-FLOW supplies 3D proposals with in-trajectory guidance, enabling cascaded test-time scaling to prune redundant backbones and terminate unpromising refinement early. Extensive experiments show that DC-FLOW improves cyclization fidelity, diversity, and interface energetics, substantially increasing the yield of confident cyclic peptide binders under a fixed final-candidate budget. Our code is available at https://github.com/mianqiu00/DCFlow.