HMAMP: Hypervolume-Driven Multi-Objective Antimicrobial Peptides Design
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 83 references that are in the map. First posted on arXiv 1 May 2024.
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References · 9
- 2018Multi-Task Learning as Multi-Objective Optimizationcited by 821
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- 2017Deep Reinforcement Learning for De-Novo Drug Designcited by 161
- 2017Long Text Generation via Adversarial Training with Leaked Informationcited by 100
- 2020Optimizing Molecules using Efficient Queries from Property Evaluationscited by 50
- 2019Multi-objective training of Generative Adversarial Networks with multiple discriminatorscited by 24
- 2020Generalized Word Shift Graphs: A Method for Visualizing and Explaining Pairwise Comparisons Between Textscited by 21
- 2015Data Generation as Sequential Decision Makingcited by 15
- 2020Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamicscited by 7
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- 2026MAC-AMP: A Closed-Loop Multi-Agent Collaboration System for Multi-Objective Antimicrobial Peptide Design
- 2025A deep reinforcement learning platform for antibiotic discovery
- 2026Agentic Discovery of Non-Canonical Antimicrobial Peptides with AMPGAN v3
- 2025OmegAMP: Targeted AMP Discovery via Biologically Informed Generation
- 2026ProDCARL: Reinforcement Learning-Aligned Diffusion Models for De Novo Antimicrobial Peptide Design
- 2022Accelerating Antimicrobial Peptide Discovery with Latent Structure
- 2026FAR-DPO: Feasibility-Aware and Robust Direct Preference Optimization for Cyclic Peptide Design
- 2026Pepti-Agent: An AI Agent for Peptide Design and Optimization