  Antimicrobial peptides (AMPs) have exhibited unprecedented potential as
biomaterials in combating multidrug-resistant bacteria. Despite the increasing
adoption of artificial intelligence for novel AMP design, challenges pertaining
to conflicting attributes such as activity, hemolysis, and toxicity have
significantly impeded the progress of researchers. This paper introduces a
paradigm shift by considering multiple attributes in AMP design.
  Presented herein is a novel approach termed Hypervolume-driven
Multi-objective Antimicrobial Peptide Design (HMAMP), which prioritizes the
simultaneous optimization of multiple attributes of AMPs. By synergizing
reinforcement learning and a gradient descent algorithm rooted in the
hypervolume maximization concept, HMAMP effectively expands exploration space
and mitigates the issue of pattern collapse. This method generates a wide array
of prospective AMP candidates that strike a balance among diverse attributes.
Furthermore, we pinpoint knee points along the Pareto front of these candidate
AMPs. Empirical results across five benchmark models substantiate that
HMAMP-designed AMPs exhibit competitive performance and heightened diversity. A
detailed analysis of the helical structures and molecular dynamics simulations
for ten potential candidate AMPs validates the superiority of HMAMP in the
realm of multi-objective AMP design. The ability of HMAMP to systematically
craft AMPs considering multiple attributes marks a pioneering milestone,
establishing a universal computational framework for the multi-objective design
of AMPs.
