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.

通过考虑多个属性，提出了一种名为 HMAMP 的新颖方法，将强化学习和基于超体积最大化概念的梯度下降算法相结合，以实现多属性抗微生物肽的同时优化。该方法生成了一系列平衡多种属性的潜在抗微生物肽候选者，并确定了这些候选者的 Pareto 前沿中的关键点。实证结果证实，HMAMP 设计的抗微生物肽具有竞争性能和增强的多样性，而对十个潜在候选抗微生物肽的螺旋结构和分子动力学模拟的详细分析验证了 HMAMP 在多属性抗微生物肽设计领域的优越性。HMAMP 能够系统地设计考虑多个属性的抗微生物肽，标志着建立一个通用的计算框架，用于多目标抗微生物肽设计的开创性里程碑。