RT Journal Article SR Electronic T1 Serverless Prediction of Peptide Properties with Recurrent Neural Networks JF bioRxiv FD Cold Spring Harbor Laboratory SP 2022.05.18.492545 DO 10.1101/2022.05.18.492545 A1 Ansari, Mehrad A1 White, Andrew D. YR 2022 UL http://biorxiv.org/content/early/2022/05/19/2022.05.18.492545.abstract AB We present three deep learning sequence prediction models for hemolysis, solubility, and resistance to non-specific interactions of peptides that achieve comparable results to the state-of-the art models. These predictive models share a common architecture of bidirectional recurrent neural networks (LSTM). These models are implemented in JavaScript so that they can be run on a static website without use of a dedicated server. This removes the cost, and long-term management of a server, while still enabling open and free access to the models. This “serverless” prediction model is a demonstration of edge computing bioinformatics and removes the dependence on cloud providers or self-hosting of resource-rich academic institutions. This is feasible because of the continued track of Moore’s law and ubiquitous hardware acceleration of deep learning computations on new phones and desktops.Competing Interest StatementThe authors have declared no competing interest.