Accessible AI for molecule generation & optimisation
DeepMirror App is our intuitive AI powered cloud app that assists medicinal chemists who want to find effective and safe drug compounds by reducing the number of DMTA cycles required to find the optimal compound and suggesting creative and potent novel molecules.
Ultimately accelerating preclinical drug discovery by up to 50% and saving companies millions.
Get in touch to request access to the app
Biologics (DNA/RNA and peptides) support coming soon.
Accessible AI
Do not waste time and money on AI. Get started with drug property and potency prediction within a few hours and see where AI can add value in your drug discovery programme.
Enhance creativity
Use generative AI to instantly create novel compounds to enhance creativity and escape flat SAR to reduce programme failure rates
50% faster Hit-to-candidate
Get from hit to lead candidate up to 50% faster by smart compound prioritisation and save millions in unnecessary synthesis
Features
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
Case Studies
We simulated small molecule lead optimisation workflows both with and without DeepMirror Chem and estimate that we enable users to find the most optimal drug up to ~4x faster.
4x acceleration of small molecule lead optimisation
In this case study, we use DeepMirror Chem to learn and predict PROTAC properties to accelerate PROTAC discovery
Predict PROTAC affinity and degradation
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 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
in csv and sdf format
Upload and merge datasets seamlessly
molecules based on molecular scaffolds
Visualise, align, and compare molecules
based on your dataset with generative AI
Generate new molecules
molecules to compare them
and prioritise
Filter, sort and pin
such as Ligand Efficiency and Synthetic Accessibility
Calculate molecular properties
using cutting-edge AI from as few as 40 compounds
Predict properties
molecules based on predictions and confidence estimates
Prioritise
with your team or collaborators
Share results
