MOTIVATION: The widespread use of antibiotics has led to the emergence of resistant pathogens. Antimicrobial peptides (AMPs) combat bacterial infections by disrupting the integrity of cell membranes, making it challenging for bacteria to develop resistance. Consequently, AMPs offer a promising solution to addressing antibiotic resistance. However, the limited availability of natural AMPs cannot meet the growing demand. While deep learning technologies have advanced AMP generation, conventional models often lack stability and may introduce unforeseen side effects. RESULTS: This study presents a novel denoising VAE-based model guided by desirable physicochemical properties for AMP generation. The model integrates key features (e.g. molecular weight, isoelectric point, hydrophobicity, etc.), and employs position encoding along with a Transformer architecture to enhance generation accuracy. A customized loss function, combining reconstruction loss, KL divergence, and property preserving loss ensure effective model training. Additionally, the model incorporates a denoising mechanism, enabling it to learn from perturbed inputs, thus maintaining performance under limited training data. Experimental results demonstrate that the proposed model can generate AMPs with desirable functional properties, offering a viable approach for AMP design and analysis, which ultimately contributes to the fight against antibiotic resistance. AVAILABILITY AND IMPLEMENTATION: The data and source codes are available both in GitHub (https://github.com/David-WZhao/PPGC-DVAE) and Zenodo (DOI 10.5281/zenodo.14730711).

Antimicrobial peptides (AMPs) are crucial in addressing the global crisis of bacterial resistance. However, there are still significant limitations in existing methods on de novo AMPs design, especially in designing AMPs with desirable physicochemical properties for specific bacterial pathogens. In this study, we propose a novel generative framework for designing pathogen-targeted antimicrobial peptides with programmable physicochemical properties. More specifically, a conditional Variational Autoencoder is first pretrained for generating AMPs with editable physicochemical properties. We then develop a conditional diffusion model to learn hidden representations of AMPs for targeting pathogens of interest, and construct corresponding MIC predictors for specific bacterial strains. Through comprehensive simulation experiments, we demonstrate that the proposed framework outperforms most existing models in terms of antimicrobial efficacy against specific bacterial targets. Moreover, through systematic screening and analysis, we have identified two star AMPs for each of the two target bacterial species (i.e., E. coli or S. aureus), both of which exhibit excellent performance in antibacterial activity, hemolytic properties, toxicity profiles, etc. Overall, this study provides the key technological support for developing next-generation intelligent platforms for antimicrobial agents design.

With the widespread use of antibiotics, many pathogens have gradually developed resistance. However, antimicrobial peptides (AMPs) exert their effects by directly disrupting the integrity of the cell membrane, making it difficult for bacteria to develop resistance through genetic mutations. This makes AMPs an important agent in addressing the issue of antibiotics resistance globally. Since the naturally obtained AMPs are limited in quantity and insufficient to meet diverse demands, the emergence of deep learning has led to breakthroughs in the field of synthetic AMPs. However, models that generate AMPs unconditionally often suffer from poor interpretability and potential side effects. In this paper, we propose a novel model for AMPs generation via physico-chemical property-guided VAE. This model comprehensively considers various physicochemical properties such as molecular weight, isoelectric point, hydrophobicity, and etc. By integrating positional encoding and a transformer structure, it effectively learns the generative process of AMPs with required physicochemical properties. Moreover, an innovative loss function is designed, which combines the reconstruction loss, KL divergence, and attribute mapping loss to effectively train the conditional VAE. Experimental results demonstrate that this model is able to generate AMPs with desirable functional characteristics, providing a feasible solution to the design and analysis of AMPs.