top of page
Background image

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

Design-Make-Test-Analyse cycle involved in biologics discovery (peptides, protein, DNA and RNA)


Properties of biologics (peptides, RNA) within minutes using our optimised autoML infrastructure

Reduce bias


Biologics for testing and application with confidence estimates and multi-parameter optimisation

Fast-track discovery


Novel and promising compounds with generative AI and iterative design

Enhance creativity
Interface of DeepMirror Bio webapp
Interface of DeepMirror Bio webapp
Dataset icon
Icons for a variety of Machine Learning model
Graph icon
Filled-in dataset icon

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

Our technology: Small Data AI

Case Studies

Small Molecules
Antibodies & Peptides

Supported Biologic Data

Nucleotide Sequences

Amino Acid

Small Molecules
Antibodies & Peptides

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

Thumbnail of CRISPR versus 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.

Coming soon...

Predicting cell free protein expression yield

Thumbnail of cell free protein expression symbolic image
bottom of page