Zero-Shot Cyclic Peptide Design via Composable Geometric Constraints

TL;DR

This paper introduces CP-Composer, a zero-shot generative framework for cyclic peptide design that uses composable geometric constraints, enabling the creation of target-specific cyclic peptides without extensive training data.

Contribution

The paper presents a novel diffusion-based model that incorporates geometric constraints for zero-shot cyclic peptide generation, addressing data scarcity in peptide design.

Findings

Achieved success rates from 38% to 84% across different cyclization strategies.

Generated diverse cyclic peptides capable of target binding.

Demonstrated effectiveness despite training only on linear peptides.

Abstract

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.