Thanks for visiting Scientific Computing World.

You're trying to access an editorial feature that is only available to logged in, registered users of Scientific Computing World. Registering is completely free, so why not sign up with us?

By registering, as well as being able to browse all content on the site without further interruption, you'll also have the option to receive our magazine (multiple times a year) and our email newsletters.

Optibrium introduces augmented chemistry services

Share this on social media:

Optibrium has announced the introduction of its Augmented Chemistry service, which provides collaborators with artificial intelligence (AI) technologies to supplement their skills and experience. Enabling them to make more effective decisions and advance their drug discovery projects. 

In discovery projects, it is important to base decisions on reliable data to avoid wasted effort pursuing incorrectly selected compounds or missing opportunities by inappropriately discarding potentially valuable compounds. 

Dr Matthew Segall, Optibrium’s CEO, said: ‘Optibrium has an extensive track record of successfully introducing and delivering cutting-edge technologies to drug discovery, and our intimate knowledge of the unique challenges of drug discovery and the underlying data enable us to go beyond the hype and deliver results that make a difference. After successfully demonstrating its capabilities in collaboration with our partners, we’re excited to now be able to bring Augmented Chemistry services to our global customer base.’

Augmented Chemistry services are built on a unique deep learning capability for data imputation, Alchemite, which has been developed in collaboration with Intellegens. Alchemite has been demonstrated to outperform traditional predictive models, both in benchmarking studies [1] and in partnerships with global pharma and biotech research organisations. 

Unlike conventional machine learning approaches, Alchemite learns simultaneously across all experimental endpoints in a project or corporate database, even based on limited data. The resulting model can automatically highlight the most confident, and therefore accurate, predictions on which to base experimental decisions, including the identification of new or previously overlooked high-quality compounds.