# Recent advances in computational antimicrobial peptide discovery through big data, modeling, and artificial intelligence and their interplay in ushering the next golden era of drug development

## TL;DR

This paper explores how big data, modeling, and AI are transforming the discovery of antimicrobial peptides to combat antibiotic resistance.

## Contribution

The paper unifies computational strategies for antimicrobial peptide discovery, highlighting their synergies and limitations.

## Key findings

- Computational methods enable efficient in silico evaluation of peptide-target interactions and membrane disruption.
- AI techniques predict and design novel antimicrobial peptides from genomic data with high accuracy.
- Integration of molecular simulations and AI offers deeper insights into AMP mechanisms and accelerates discovery.

## Abstract

The accelerating antimicrobial resistance (AMR) crisis continues to render more and more conventional antibiotics ineffective. Antimicrobial peptides (AMPs) are promising alternatives to traditional antibiotics due to their broad-spectrum activity, diverse mechanisms of action, and lower propensity for resistance. Traditional discovery approaches face limitations arising from the vast sequence space and the challenge of balancing efficacy with low toxicity. Addressing these challenges is critical for developing next-generation antimicrobial agents, and computational methods are increasingly driving progress. Public repositories, and techniques such as molecular docking enable in silico evaluation of peptide target interactions, identifying candidates with strong binding potential. Molecular dynamics (MD) simulations offer deeper insights into how AMPs disrupt membranes, form pores, or act synergistically, while Steered MD extends this to probing membrane penetration. Artificial intelligence (AI) methods, including machine learning and deep learning, capture complex sequence activity relationships, predict novel AMPs from genomic and metagenomic data, and design new peptides de novo using generative models. Despite rapid advances, most existing reviews treat these approaches in isolation, leaving a fragmented understanding of their interplay. This paper addresses that gap by unifying computational strategies, highlighting synergies, and critiquing limitations. Ultimately, integrating these methodologies offers a path toward more efficient AMP discovery to fight AMR.