Deep learning holds a big promise for optimizing existing peptides with more
desirable properties, a critical step towards accelerating new drug discovery.
Despite the recent emergence of several optimized Antimicrobial peptides(AMP)
generation methods, multi-objective optimizations remain still quite
challenging for the idealism-realism tradeoff. Here, we establish a
multi-objective AMP synthesis pipeline (MoFormer) for the simultaneous
optimization of multi-attributes of AMPs. MoFormer improves the desired
attributes of AMP sequences in a highly structured latent space, guided by
conditional constraints and fine-grained multi-descriptor.We show that MoFormer
outperforms existing methods in the generation task of enhanced antimicrobial
activity and minimal hemolysis. We also utilize a Pareto-based non-dominated
sorting algorithm and proxies based on large model fine-tuning to
hierarchically rank the candidates. We demonstrate substantial property
improvement using MoFormer from two perspectives: (1) employing molecular
simulations and scoring interactions among amino acids to decipher the
structure and functionality of AMPs; (2) visualizing latent space to examine
the qualities and distribution features, verifying an effective means to
facilitate multi-objective optimization AMPs with design constraints

利用深度学习优化多目标抗菌肽，通过多目标合成管道（MoFormer）在结构化潜空间中引导和改善抗菌肽序列的属性以提高抗菌活性和减少溶血，同时使用非支配排序算法和大模型微调的代理方法进行候选者的层次排名，通过分子模拟和可视化潜空间来追踪和优化肽的结构和功能，验证了多目标优化的有效手段。