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 Daniel Barr to hear more about how deep learning imputation prioritises the most relevant data, accounts for uncertainty, and guides experiment selection to bring additional value to small molecule discovery.
Join Optibrium’s Chris Khoury at the 38th NMCS meeting in Seattle, 23-26 June
Join Optibrium at HubXchange in Boston to hear more about implementing AI in drug discovery.
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Out now in International Biopharmaceutical Industry, Optibrium’s CEO, Dr Matt Segall introduces the concept of augmented intelligence. He explains how to use dynamic…
Out now in Innovations in Pharmaceutical Technology, Optibrium’s Global Head of Application Science and President of Optibrium Inc, Dr Tamsin Mansley discusses…
In European Biopharmaceutical Review, Optibrium’s CEO Dr Matthew Segall discusses how we can elevate drug discovery with deep learning imputation. He shares…
Now, watch Matt Segall, PhD, CEO at Optibrium, as he introduces a real world case study where we applied deep learning to guide a project, in which potential compounds were displaying good activity profiles but the team wanted to improve their PK profile to achieve better efficacy.
In this webinar, learn about Cerella’s unique AI methods, see examples of its successful application throughout the drug discovery process and watch a demonstration of how CDD Vault and Cerella connect to seamlessly integrate with your workflows.
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.
In this webinar, Jeff Blaney (Senior Director of Discovery Chemistry, Genentech), Darren Green (Head of Cheminformatics & Data Science, GlaxoSmithKline), Julian Levell (Head of Discovery, New Equilibrium Biosciences), Matthew Segall (CEO, Optibrium) discuss the state of AI in early drug discovery from hit to preclinical candidate and share their experiences with and expectations of AI, including predictive modelling, synthesis prediction, and generative chemistry. Hear about the successes of AI drug discovery and an outlook on what AI needs to achieve to really transform the industry.
This article is a collaboration with Intellegens, the University of Cambridge and AstraZeneca. It provides a proof-of-concept study in which Cerella™ is used to predict rat in vivo pharmacokinetic (PK) parameters and concentration–time PK profiles.
In this study, we identified a new antimalarial with an unusual structure – the only compound in the competition to be proven active, opening up new chemistry for exploration.
In this article, the team demonstrates the application of Alchemite™, a deep learning imputation method which underpins our Cerella™ technology, to physicochemical and sensory data.
In this webinar, we demonstrate how Augmented Chemistry®, a unique deep learning method, can learn from higher throughput data together with limited panel data to provide high-quality imputations for sensory properties.
In this webinar, we discuss Alchemite™, a novel deep learning approach, and its application to optimising kinase profiling programmes. The…
OA paper outlining the practical applications of deep imputation on large-scale drug discovery data. It compares deep learning to traditional QSAR methods.
In this webinar, we described the generation and validation of a ‘global’ model using deep learning imputation on a data set of 300,000 compounds and 500 experimental endpoints, targeting global health indications.
We demonstrated how this global model can be applied to individual optimisation projects, offering improved compounds design performance over ‘local’ project-specific models by learning across a broad chemical diversity.
