# HMAMP: Hypervolume-Driven Multi-Objective Antimicrobial Peptides Design

arXiv: https://arxiv.org/abs/2405.00753 · HTML: https://mlsanity.com/paper/2405.00753/

## 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.

## Builds on (most cited first, 9 of 9)

- [Multi-Task Learning as Multi-Objective Optimization](https://mlsanity.com/paper/1810.04650/) (2018)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [Deep Reinforcement Learning for De-Novo Drug Design](https://mlsanity.com/paper/1711.10907/) (2017)
- [Long Text Generation via Adversarial Training with Leaked Information](https://mlsanity.com/paper/1709.08624/) (2017)
- [Optimizing Molecules using Efficient Queries from Property Evaluations](https://mlsanity.com/paper/2011.01921/) (2020)
- [Multi-objective training of Generative Adversarial Networks with multiple discriminators](https://mlsanity.com/paper/1901.08680/) (2019)
- [Generalized Word Shift Graphs: A Method for Visualizing and Explaining Pairwise Comparisons Between Texts](https://mlsanity.com/paper/2008.02250/) (2020)
- [Data Generation as Sequential Decision Making](https://mlsanity.com/paper/1506.03504/) (2015)
- [Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics](https://mlsanity.com/paper/2005.11248/) (2020)

## 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.