  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
