Go from drug concept to candidate, faster

Proven AI-powered drug discovery

Cerella creates new value from your drug discovery data, revealing hidden insights into the best compounds and most valuable experiments for your project.

It makes confident predictions, accurately filling in missing values, especially for expensive downstream experiments that can’t be predicted by other methods. This enables you to do more, even with sparse, limited data sets.

Drug discovery data is complex

With endless possible chemical space to explore and experiments to perform, how do you choose the best compounds and prioritise your resources?

Add to this the inevitability of errors and uncertainty, and you get your next challenge – how can you trust the data which you do have?

You know how important it is to find high-quality candidate drugs, fast. A streamlined workflow is essential, but complex structure-activity relationships, hidden mechanisms of action and adverse outcome pathways make it tricky to see which compounds to progress and the experiments that can be cut.

Want to gain a competitive advantage?

AI-guided drug discovery: Cerella video screenshot

Discover Cerella

Use early-stage data to predict late-stage outcomes, avoiding unnecessary and expensive measurements

Uncover hidden opportunities caused by missing, uncertain or inaccurate data

Create value from all your data, informing future strategies by harnessing existing data from parallel or previous projects

Identify the most valuable measurements which will maximise the return on investment of your experimental efforts

Target high-quality compounds with confidence, using robust uncertainty estimates

By integrating machine learning and AI into our R&D efforts we expect to identify promising compounds and rapidly optimize their properties which will accelerate our pipeline. We aim to revolutionize how we develop sustainable solutions for farmers enhance global food security and trust that Optibrium’s software solutions will get us there.
Dr. Seva Rostovtsev, Executive VP and CTO, FMC

Interested in trying Cerella?

Get in touch to chat with us about how Cerella could improve your discovery workflows

Get in touch

Go from drug concept to candidate, faster

Proven AI-powered drug discovery

Drug discovery data is complex

Transformative insights, revealed

Use early-stage data to predict late-stage outcomes, avoiding unnecessary and expensive measurements

Uncover hidden opportunities caused by missing, uncertain or inaccurate data

Create value from all your data, informing future strategies by harnessing existing data from parallel or previous projects

Identify the most valuable measurements which will maximise the return on investment of your experimental efforts

Target high-quality compounds with confidence, using robust uncertainty estimates

Interested in trying Cerella?

Proven success in drug discovery

See how Cerella translated in vitro data to in vivo savings for AstraZeneca.

Hear how Constellation Pharmaceuticals saved “two months’ worth of 12 chemists’ workloads” by identifying inactive compounds.

Discover how FMC used Cerella to guide purchasing and screening decisions.

Hear how Cerella predicted complex in vivo sensory responses, accurately, for International Flavors & Fragrances.

A 76% reduction in the number of assays required. See how Cerella could transform Genentech’s kinase programmes.

Learn how Cerella was able to find the only compound experimentally proven as active in the OSM competition.

Discover how Cerella handled 700,000 compounds and 1,000 experimental endpoints for Takeda Pharmaceuticals.

See how Cerella helped obtain accurate in vivo PK profiles from limited in vitro ADME data for our collaborator.

See how Cerella translated in vitro data to in vivo savings for AstraZeneca.

Hear how Constellation Pharmaceuticals saved “two months’ worth of 12 chemists’ workloads” by identifying inactive compounds.

Discover how FMC used Cerella to guide purchasing and screening decisions.

Hear how Cerella predicted complex in vivo sensory responses, accurately, for International Flavors & Fragrances.

A 76% reduction in the number of assays required. See how Cerella could transform Genentech’s kinase programmes.

Learn how Cerella was able to find the only compound experimentally proven as active in the OSM competition.

Discover how Cerella handled 700,000 compounds and 1,000 experimental endpoints for Takeda Pharmaceuticals.

See how Cerella helped obtain accurate in vivo PK profiles from limited in vitro ADME data for our collaborator.

Cerella resources

Prediction of in vivo pharmacokinetic parameters and time – exposure curves in rats using machine learning from the chemical structure

Practical applications of deep learning to impute heterogeneous drug discovery data

Imputation of assay bioactivity data using deep learning

Ready to transform to AI-guided drug discovery?