TG-CDDPM: text-guided antimicrobial peptides generation based on conditional denoising diffusion probabilistic model.

ABSTRACT: Antimicrobial peptides (AMPs) have emerged as a promising substitution to antibiotics thanks to their boarder range of activities, less likelihood of drug resistance, and low toxicity. Traditional biochemical methods for AMP discovery are costly and inefficient. Deep generative models, including the long-short term memory model, variational autoencoder model, and generative adversarial model, have been widely introduced to expedite AMP discovery. However, these models tend to suffer from the lack of diversity in generating AMPs. The denoising diffusion probabilistic model serves as a good candidate for solving this issue. We proposed a three-stage Text-Guided Conditional Denoising Diffusion Probabilistic Model (TG-CDDPM) to generate novel and homologous AMPs. In the first two stages, contr

SUBMITTER: Cao J 

PROVIDER: S-EPMC11637771 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature