  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.
