These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
pepgan_first.pdf (398.09 kB)

Generating Ampicillin-Level Antimicrobial Peptides with Activity-Aware Generative Adversarial Networks

submitted on 12.04.2020, 06:17 and posted on 13.04.2020, 13:18 by Andrejs Tucs, Duy Phuoc Tran, Akiko Yumoto, Yoshihiro Ito, Takanori Uzawa, Koji Tsuda

Antimicrobial peptides are a potential solution to the threat of multidrug-resistant bacterial pathogens. Recently, deep generative models including generative adversarial networks (GANs) have been shown to be capable of designing new antimicrobial peptides. Intuitively, a GAN controls the probability distribution of generated sequences to cover active peptides as much as possible. This paper presents a peptide-specialized model called PepGAN that takes the balance between covering active peptides and

dodging non-active peptides. As a result, PepGAN has superior statistical fidelity with respect to physicochemical descriptors including charge, hydrophobicity and weight. Top six peptides were synthesized and one of them was confirmed to be highly antimicrobial. The minimum inhibitory concentration was 3.1μg/mL, indicating that the peptide is twice as strong as ampicillin.


Email Address of Submitting Author


University of Tokyo



ORCID For Submitting Author


Declaration of Conflict of Interest

No conflict of interest