Antimicrobial resistance is an increasing issue in healthcare as the overuse of antibacterial agents rises during the COVID-19 pandemic . The need for new antibiotics is high, while the arsenal of available agents is decreasing, especially for the treatment of infections by Gram-negative bacteria like Escherichia coli . Antimicrobial peptides (AMPs) are offering a promising route for novel antibiotic development and deep learning techniques can be utilised for successful AMP design . In this study, a long short-term memory (LSTM) generative model and a bidirectional LSTM classification model were constructed to design short novel AMP sequences with potential antibacterial activity against E. coli . Two versions of the generative model and six versions of the classification model were trained and optimised using Bayesian hyperparameter optimisation . These models were used to generate sets of short novel sequences that were classified as antimicrobial or non-antimicrobial . The validation accuracies of the classification models were 81.6–88.9% and the novel AMPs were classified as antimicrobial with accuracies of 70.6–91.7% . Predicted three-dimensional conformations of selected short AMPs exhibited the alpha-helical structure with amphipathic surfaces . This demonstrates that LSTMs are effective tools for generating novel AMPs against targeted bacteria and could be utilised in the search for new antibiotics leads.