DeepMirror Bio is our service which accelerates Design-Make-Test-Analyse (DMTA) cycles for biologics (RNA, DNA, and proteins). With it, you can apply generative AI to incept ideas and prioritise biologics based on predicted properties.
Get in touch to get started with your optimisation problem.
DeepMirror Bio
PREDICT
Properties of biologics (peptides, RNA) within minutes using our optimised autoML infrastructure
PRIORITISE
Biologics for testing and application with confidence estimates and multi-parameter optimisation
DISCOVER
Novel and promising biologics with generative AI and iterative design
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
Case Studies
Our technology: Small Data AI
Upload your experimental data through our user-friendly interface
The best performing model is automatically selected for a given dataset
Experimental endpoints
are predicted using
the best model
A large library of different machine learning models are trained on a user's experimental data
Supported Biologic Data
Nucleotide Sequences
(DNA/RNA)
Amino Acid
Sequences
