Therapeutic peptides have proven to have great pharmaceutical value and
potential in recent decades. However, methods of AI-assisted peptide drug
discovery are not fully explored. To fill the gap, we propose a target-aware
peptide design method called \textsc{PPFlow}, based on conditional flow
matching on torus manifolds, to model the internal geometries of torsion angles
for the peptide structure design. Besides, we establish a protein-peptide
binding dataset named PPBench2024 to fill the void of massive data for the task
of structure-based peptide drug design and to allow the training of deep
learning methods. Extensive experiments show that PPFlow reaches
state-of-the-art performance in tasks of peptide drug generation and
optimization in comparison with baseline models, and can be generalized to
other tasks including docking and side-chain packing.

基于条件流匹配对环面进行建模的针对靶向感知的肽设计方法 PPFlow 填补了人工智能辅助肽药物发现领域的不足，并通过建立名为 PPBench2024 的蛋白 - 肽结合数据集以填充大规模数据空白，实验证明 PPFlow 在肽药物的生成和优化等任务上具有领先水平的性能，并且可以推广到其他任务，包括对接和侧链装配。

PPFlow：具有扭转流匹配的目标感知肽设计

PPFlow: Target-aware Peptide Design with Torsional Flow Matching

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。