In this ebook we demonstrate our deployable AI discovery platform, Cerella™. Browse real-world stories of success from our collaborations with AstraZeneca, Genetech, Takeda Pharmaceuticals, Constellation Pharmaceuticals and many more.
Join Optibrium’s Chris Khoury at the 38th NMCS meeting in Seattle, 23-26 June
Interpreting metabolite-ID experiments; determining the right species for animal studies; providing optimisation suggestions for your medicinal chemistry colleagues to overcome…
Semeta™ offers high sensitivity and superior precision for the prediction of Phase I and II metabolic routes, sites, products and liabilities in early drug discovery
Predicting metabolism at an early stage is important in maximising the chance of a drug’s success. However, accurate, useful models…
Peer-reviewed study published in Xenobiotica describes an innovative new method that predicts the routes and products of Phase I and II metabolism with high sensitivity and greater precision than
other approaches
This peer-reviewed paper in Xenobiotica describes a new method to determine the most likely experimentally-observed routes of metabolism and metabolites based on our WhichP450™, regioselectivity and new WhichEnzyme™ model.
Out now in Drug Target Review, Optibrium’s Director of Computational Chemistry, Dr Peter Hunt discusses why early in silico metabolism prediction is crucial…
We explore the exciting new features in the latest release of StarDrop, built to elevate your drug discovery projects. These include the all-new Metabolism module; high performance virtual screening; additional workflow improvements
Backed by six years’ research, the new StarDrop Metabolism module combines quantum mechanics and machine learning to better predict the metabolic fate of drug candidates.
The Metabolism module enables you to accurately predict the major metabolic routes, sites, products and lability of Phase I and…
This paper describes a model to predict whether a particular site on a molecule will be metabolised by cytosolic sulfotransferase enzymes (SULTs).
Watch Optibrium CEO Matt Segall and Principal Scientist Mario Öeren as they explore groundbreaking new quantum mechanics and machine learning models which go beyond P450s and provide insights on a broad range of enzymes involved in drug metabolism.
Introduction Predicting sites of metabolism (SoM) enable chemists to be more efficient in optimising the structure of new chemical entities…
Learn more about how AI, machine learning and other computational tools can support the discovery process, bringing you feasible synthetic routes to your target compounds.
In the face of growing agrochemical resistance and increasingly stringent regulatory requirements, how can artificial intelligence (AI) be harnessed to help lower the costs, failure rates and timelines associated with current agrochemical development cycles?
This paper describes the prediction of the regioselectivity of metabolism by AOs, FMOs and UGTs for humans and CYPs for three preclinical species.
In this webinar, we examine the effective use of QSAR modelling in drug discovery and discuss a variety of pain points for medicinal chemists in knowing when a model can be trusted and how to avoid common pitfalls.
