How do we do this?
When existing data alone is not sufficient for generating specific predictions, sparse-data AI comes to the rescue. Our STARMAP® 2.0 platform augments experimental results with detailed expert knowledge to allow sensible predictions to be made regarding drug development success.
Currently, we are launching a digital version of our CESS® technology that allows us to perform in silico experiments in large quantities and to create predictions of nanoformability. This is important since there are more potential drug molecules than particles in the known universe.
Why do we do this?
Our game-changing CESS® technology lies at the heart of our operations and offers a unique opportunity to both bring failed assets back to life again and accelerate APIs to the clinic. The STARMAP® platform can have wide applicability in drug discovery and development as well as in lifecycle management for existing marketed drugs and 505b2 like product development strategies.
As CESS® has the potential to drastically improve several characteristics of APIs relative to other technology platforms, we recognize that it is vital to apply STARMAP® 2.0 widely to rapidly identify for our customers the APIs with the greatest potential for nanoforming success.
Additionally, past AI-based technologies were trained on old particle engineering techniques such as micronization, limiting prediction accuracy. This opens up the possibility that previously disregarded drug candidates can be revisited with the latest technology and transformed into a drug development success story.
Ultimately, the goal of STARMAP® 2.0 is to benefit as many patients as possible by making new and better drugs available faster than is possible with other technological platforms.