Meet with us at ACS Fall 2024
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.
38th ACS National Medicinal Chemistry Symposium
Join Optibrium’s Chris Khoury at the 38th NMCS meeting in Seattle, 23-26 June
Does your model know its limits? Leveraging uncertainties to find better compounds
If you’re using predictive models in your molecule design and optimisation, an accurate uncertainty estimate can be just as important…
Rapid AI generation of optimised compound designs, guided by user interaction
Pairing AI with human expertise We present a novel AI compound optimisation system, designed to include human oversight as a…
Combining deep learning with multi-parameter optimisation to predict compounds with selective activity against a broadleaf weed species
Introduction The emergence of resistance and increased stringency of regulatory requirements have created a need for new agrochemicals. The long…
Explaining the ‘black box’: deep learning in drug discovery
Recent years have seen a remarkable rise in the number and scope of artificial intelligence and machine learning (especially deep…
Avoid the hype – how to successfully implement AI in drug discovery
AI has the potential to transform discovery. However, to ensure real impact, there are several practicalities that organisations must consider…
An augmented approach to generative chemistry
Generative molecular design provides new exciting avenues of chemical space exploration. But how can we use these methods effectively to assess many optimisation strategies and find the compounds destined for success in our projects?
Join Dr Matt Segall and Dr Michael Parker as they explore state-of-the-art generative chemistry, and discuss the importance of an augmented intelligence approach for successful discovery.
Perspectives on generative chemistry – potential and reality
Have advances in AI and deep learning reached a threshold whereby generative chemistry methods are redefining drug design? This webinar…
AI in early drug discovery: from promise to practice
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.
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.
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.
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.
Imputation of sensory properties using deep learning: webinar
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.
Harnessing generative chemistry – how scientists can guide compound idea generation
Why have generative chemistry methods been unable to redefine modern drug discovery and compound idea generation?’ In this session we shed light on a typical shortcoming of generative methods related to prioritising promising over unsuitable directions for exploration.
3D ligand-based drug design: webinar
In this webinar, we demonstrate intuitive workflows for 3D ligand-based drug design
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.
A global deep learning model for global health drug discovery
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.
