DeepMirror Chem is our web app that accelerates Design-Make-Test-Analyse (DMTA) cycles in preclinical drug discovery by 4x.
In the app, you can predict compound potency and ADMET from previous experiments and use generative AI to ideate novel structures.
Get in touch to request access to our app.
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
QSAR and ADMET properties within minutes using our optimised autoML infrastructure
Compounds for synthesis with confidence estimates and multi parameter optimisation
Novel and promising compounds with generative AI and iterative design
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
are predicted using
the best model
A large library of different machine learning models are trained on a user's experimental data
Small Data AI
Predict QSAR and ADMET from small datasets or no data at all
Our platform automatically cycles through ~100 algorithms to deliver the best possible predictions
Use validated prediction confidence estimates to inform your next step
Safe and private cloud computing (ISO27001 and SOC2 certification in progress)
Easy to use, no-code software so you don't need to know how to program to use our technology
Generate in silico small molecule libraries using Generative AI