A Multi-Modal Contrastive Diffusion Model for Therapeutic Peptide Generation

TL;DR

This paper introduces a multi-modal diffusion model that combines peptide sequence and structure data using contrastive learning to improve the generation of therapeutic peptides, outperforming existing methods.

Contribution

The novel multi-modal contrastive diffusion framework effectively integrates sequence and structure modalities for peptide generation, enhancing performance over prior single-modal models.

Findings

Outperforms state-of-the-art generative methods in peptide quality metrics

Improves diversity and therapeutic relevance of generated peptides

Demonstrates superior docking scores and biological activity predictions

Abstract

Therapeutic peptides represent a unique class of pharmaceutical agents crucial for the treatment of human diseases. Recently, deep generative models have exhibited remarkable potential for generating therapeutic peptides, but they only utilize sequence or structure information alone, which hinders the performance in generation. In this study, we propose a Multi-Modal Contrastive Diffusion model (MMCD), fusing both sequence and structure modalities in a diffusion framework to co-generate novel peptide sequences and structures. Specifically, MMCD constructs the sequence-modal and structure-modal diffusion models, respectively, and devises a multi-modal contrastive learning strategy with intercontrastive and intra-contrastive in each diffusion timestep, aiming to capture the consistency between two modalities and boost model performance. The inter-contrastive aligns sequences and structures of peptides by maximizing the agreement of their embeddings, while the intra-contrastive differentiates therapeutic and non-therapeutic peptides by maximizing the disagreement of their sequence/structure embeddings simultaneously. The extensive experiments demonstrate that MMCD performs better than other state-of-theart deep generative methods in generating therapeutic peptides across various metrics, including antimicrobial/anticancer score, diversity, and peptide-docking.