26th North American ISSX and JSSX Meeting
The joint ISSX/JSSX meeting is for researchers looking to gain a deeper understanding of drug metabolism and pharmacokinetics.
Cerella case studies ebook
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.
38th ACS National Medicinal Chemistry Symposium
Join Optibrium’s Chris Khoury at the 38th NMCS meeting in Seattle, 23-26 June
Overcoming challenges in drug metabolism: in silico approaches
Interpreting metabolite-ID experiments; determining the right species for animal studies; providing optimisation suggestions for your medicinal chemistry colleagues to overcome…
Optibrium launches a metabolism prediction software platform tailored to DMPK scientists
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
Transferable machine learning interatomic potential for bond dissociation energy prediction of drug-like molecules
Predicting metabolism at an early stage is important in maximising the chance of a drug’s success. However, accurate, useful models…
Optibrium’s quantum mechanics and machine learning methods predict routes of drug metabolism
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
Predicting routes of Phase I and II metabolism based on quantum mechanics and machine learning
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.
Models for success: improving drug metabolism prediction
Out now in Drug Target Review, Optibrium’s Director of Computational Chemistry, Dr Peter Hunt discusses why early in silico metabolism prediction is crucial…
Metabolism prediction, 3D virtual screening and more – meet StarDrop 7.5
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
Optibrium releases powerful metabolism prediction capability in next generation StarDrop software
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.
Introduction to the Metabolism module
The Metabolism module enables you to accurately predict the major metabolic routes, sites, products and lability of Phase I and…
Predicting regioselectivity of cytosolic SULT metabolism for drugs
This paper describes a model to predict whether a particular site on a molecule will be metabolised by cytosolic sulfotransferase enzymes (SULTs).
Integrated prediction of Phase I and II metabolism
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.
Predicting reactivity to drug metabolism: beyond CYPs
Introduction Predicting sites of metabolism (SoM) enable chemists to be more efficient in optimising the structure of new chemical entities…
The power of AI applied to agrochemical bioactivity
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?
Predicting regioselectivity of AO, CYP, FMO and UGT metabolism using quantum mechanical simulations and machine learning
This paper describes the prediction of the regioselectivity of metabolism by AOs, FMOs and UGTs for humans and CYPs for three preclinical species.
