Physicochemical Property-guided Conditional VAE for Antimicrobial Peptides Generation

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.