HMAMP: Hypervolume-Driven Multi-Objective Antimicrobial Peptides Design

TL;DR

HMAMP introduces a novel multi-objective reinforcement learning approach that optimizes antimicrobial peptides across multiple attributes, enhancing diversity and performance in drug design.

Contribution

This paper presents HMAMP, a hypervolume-driven reinforcement learning method for multi-objective AMP design, addressing conflicting attributes and expanding exploration space.

Findings

HMAMP generates diverse, high-performing AMP candidates.

Empirical validation shows superior performance over benchmarks.

Structural and molecular dynamics analyses confirm candidate efficacy.

Abstract

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.