Zero-Shot Cyclic Peptide Design via Composable Geometric Constraints
This addresses the challenge of cyclic peptide design for medical applications, representing a novel method for a known bottleneck.
The paper tackles the problem of designing target-specific cyclic peptides with limited training data by proposing CP-Composer, a generative framework that uses composable geometric constraints, achieving success rates from 38% to 84% in generating diverse cyclic peptides.
Cyclic peptides, characterized by geometric constraints absent in linear peptides, offer enhanced biochemical properties, presenting new opportunities to address unmet medical needs. However, designing target-specific cyclic peptides remains underexplored due to limited training data. To bridge the gap, we propose CP-Composer, a novel generative framework that enables zero-shot cyclic peptide generation via composable geometric constraints. Our approach decomposes complex cyclization patterns into unit constraints, which are incorporated into a diffusion model through geometric conditioning on nodes and edges. During training, the model learns from unit constraints and their random combinations in linear peptides, while at inference, novel constraint combinations required for cyclization are imposed as input. Experiments show that our model, despite trained with linear peptides, is capable of generating diverse target-binding cyclic peptides, reaching success rates from 38% to 84% on different cyclization strategies.