Scalable Peptide Design via Memory-Efficient Equivariant Transformer

2026-06-23

AI Summary

This work addresses the challenge of efficiently generating target-specific peptides with coordinated sequence and structure design under full-atom geometric constraints. To this end, the authors propose MEET, a memory-efficient E(3)-equivariant Transformer that achieves linear memory scaling during encoding, decoding, and denoising by jointly propagating scalar and vector feature streams. The architecture innovatively reformulates geometric computations into a memory-efficient attention mechanism, initializing vector features via global coordinate aggregation, incorporating distance-augmented dot products, and injecting covalent bond constraints through sparse key adaptation. Integrated within a variational autoencoder and latent diffusion framework, MEET significantly enhances the binding affinity, physical plausibility, and diversity of generated peptides on the large-scale AFDB dataset, outperforming current state-of-the-art methods.

Abstract

Target-specific peptide design requires sequence and structure co-design under full atom geometric constraints. Latent generative frameworks offer an effective route for this problem by compressing fine grained atomic structures into block level latent representations and performing conditional generation in a compact latent space. However, the scalability of such systems depends heavily on the geometric backbone used throughout their encoding, decoding, and denoising components. We introduce MEET (Memory Efficient Equivariant Transformer), an E(3) equivariant backbone for scalable atomistic peptide modeling. MEET maintains coupled invariant scalar and equivariant vector feature streams, while reformulating geometric computation around memory efficient attention. It initializes vector features through global coordinate aggregation, incorporates pairwise distances through augmented query and key dot products, and injects covalent bond information through sparse bond adaptation. Integrated into a VAE and latent diffusion pipeline for full atom peptide generation, \model{} achieves linear memory scaling with atom count and improves generation quality over existing peptide design methods. Experiments on large scale AFDB derived datasets further show that the proposed backbone supports systematic model and data scaling, leading to better binding affinity, physical validity, and sample diversity.

Problem

• peptide design
• full atom geometric constraints
• scalability
• memory efficiency
• E(3) equivariance

Innovation

• Equivariant Transformer
• Memory Efficiency
• Peptide Design
• Latent Diffusion
• Geometric Deep Learning