Synthetic promoter design in Escherichia coli based on multinomial diffusion model

Generative design of promoters has enhanced the efficiency of <i>de novo</i> creation of functional sequences. Though several deep generative models have been employed in biological sequence generation, including variational autoencoder (VAE) or Wasserstein generative adversarial network (WGAN), these models might struggle with mode collapse and low sample diversity. In this study, we introduce the multinomial diffusion model (MDM) for promoter sequence design and propose a structured set of criteria for effectively comparing the performance of generative models. <i>In silico</i> experiments demonstrate that MDM outperforms existing generative AI approaches. MDM demonstrates superior performance in various computational evaluations, remains robust during the training process, and exhibits a strong ability in capturing weak signals. In addition, we experimentally validated that the majority of our model designed promoters have expression activities <i>in vivo</i>, indicating the practicality and potential of MDM for bioengineering.