Our autoML infrastructure learns biological endpoints from customer data to predict on new data
Select promising DNA/RNA or protein candidates for further testing with confidence measures and multiparameter optimisation
Use explainable AI to unravel mechanisms of action and highlight substructures that indicate positive results
DeepMirror Bio learns which gRNA sequence and secondary structure motifs correlate with high prime editing efficiency. Predictions can then be used to select optimal guides.
Identifying optimal gRNAs for Prime Editing
DeepMirror Bio uses structural prediction algorithms (such as AlphaFold) and Graph Machine Learning to learn protein properties such as the yield in a cell free protein expression system.
Predicting cell free protein expression yield
Our technology: Small Data AI
Our technology, called Small Data AI unifies 3 key capabilities: (1) structural representations of biomolecules, (2) self/semi-supervised learning from partially labelled data with less than 10k labels, and (3) motif detections in structural representations. Feel free to click on each step for further information.
Using structural representations of data enables learning in small data scenarios as structure diversity alone carries information that can be harnessed to make predictive models. Additionally, structural models can be queried for “explainability” so that our models are not black box.