PT  - JOURNAL ARTICLE
AU  - Mehrad Ansari
AU  - Andrew D. White
TI  - Serverless Prediction of Peptide Properties with Recurrent Neural Networks
AID  - 10.1101/2022.05.18.492545
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.05.18.492545
4099  - http://biorxiv.org/content/early/2022/05/19/2022.05.18.492545.short
4100  - http://biorxiv.org/content/early/2022/05/19/2022.05.18.492545.full
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.