Deploying Cerella for active learning using deep learning imputation
In this webinar we demonstrated how this new platform provides interactive access to deep learning imputation to extract more value…
Large scale imputation of drug discovery data using deep learning
We presented a case study in which Alchemite was applied to a data set comprising approximately 700,000 compounds and 1,000…
AI-guided design of antimalarials with In vitro validation
In this webinar you can hear how a combination of generative chemistry and deep learning has impacted the design of…
Practical applications of deep learning to imputation of drug discovery data
In this webinar, we explore how the limitations of pharmaceutical data can impact conventional predictive model building. Our speakers, Julian Levell (Constellation pharmaceuticals), Ben Irwin and matt Segall (Optibrium) demonstrate how the deep learning imputation algorithm underlying our Cerella platform, overcomes these challenges.
Transforming drug discovery with deep learning imputation
In European Biopharmaceutical Review, Optibrium’s CEO Dr Matthew Segall discusses how we can elevate drug discovery with deep learning imputation. He shares…
Imputation of assay bioactivity data using deep learning
This article describes a novel deep learning neural network method and its application for the imputation of bioactivity data, such…
Prediction of in vivo pharmacokinetic parameters and time – exposure curves in rats using machine learning from the chemical structure
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.
Imputation of sensory properties using deep learning
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.
Practical applications of deep learning to impute heterogeneous drug discovery data
This article outlines practical applications of deep learning on drug discovery data. It introduces some of the research behind our…
AI in drug discovery – HubXchange
Join Optibrium at HubXchange in Boston to hear more about implementing AI in drug discovery.
Augmenting Inspiration with generative chemistry
Out now in International Biopharmaceutical Industry, Optibrium’s CEO, Dr Matt Segall introduces the concept of augmented intelligence. He explains how to use dynamic…
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.
Experimental validation of predictive models in a series of novel antimalarials
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.
Deep imputation on large-scale drug discovery data
OA paper outlining the practical applications of deep imputation on large-scale drug discovery data. It compares deep learning to traditional QSAR methods.
AI in the drug discovery industry
Out now in Innovations in Pharmaceutical Technology, Optibrium’s Global Head of Application Science and President of Optibrium Inc, Dr Tamsin Mansley discusses…
A practical guide to implementing AI
In this ebook, you’ll discover the key considerations which every leader needs to take in order to successfully implement AI in their drug discovery pipelines.
