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.

本研究提出了一种多模态对比扩散模型（MMCD），将序列和结构两种模态在扩散框架中融合，旨在共同生成新的多肽序列和结构。MMCD 通过构建序列模态和结构模态的扩散模型，并在每个扩散步骤中采用多模态对比学习策略，通过交叉对比和内部对比来捕捉两种模态之间的一致性，提高模型性能。广泛的实验表明，MMCD 在生成治疗性肽段方面优于其他最先进的深度生成方法，包括抗菌 / 抗癌评分、多样性和肽段对接等多个指标。

治疗性肽生成的多模态对比扩散模型

A Multi-Modal Contrastive Diffusion Model for Therapeutic Peptide  Generation

Protein engineering seeks to identify protein sequences with optimized
properties. When guided by machine learning, protein sequence generation
methods can draw on prior knowledge and experimental efforts to improve this
process. In this review, we highlight recent applications of machine learning
to generate protein sequences, focusing on the emerging field of deep
generative methods.

该论文综述了近期将机器学习应用于蛋白质序列优化的发展，重点介绍了深度生成方法对这一过程的促进作用。