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
AI in drug discovery – HubXchange
Join Optibrium at HubXchange in Boston to hear more about implementing AI in drug discovery.
How to be a great drug discovery chemist ebook
Discover the skills, knowledge and tools which are essential for success for today’s drug hunters.
Optibrium introduces cloud-based version of StarDrop drug discovery platform
Cloud-based version of StarDrop retains all the functionality and interactivity of desktop version with improved accessibility and lower total cost of ownership for customers.
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…
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…
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…
Real world case studies: Predicting pharmacokinetics from limited ADME data with deep learning
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.
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?
Transformative insights from your data in CDD vault with Cerella AI
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.
Perfecting the use of imperfect QSAR models
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.
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.
AI Solutions from hit to candidate
In this webinar, we explore the highlights of collaborative project results that demonstrate how every phase of the drug discovery process can be radically improved by applying proven AI technology. Providing scientists with insights on which to base decisions can identify valuable new opportunities and reduce the time and cost of AI drug discovery cycles.
We review case studies from collaborations with Constellation Pharmaceuticals, AstraZeneca, Genentech, the University of Dundee and Takeda Pharmaceuticals to validate the impact of applying AI to experimental data and illustrate dramatic improvements to their project outcomes.
Join Samar Mahmoud and Matt Segall for this fascinating deep dive into the revolution that AI is bringing to the challenges of sparse and noisy drug discovery data.
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.
Optimising kinase profiling programmes with deep learning
In this webinar, we discuss Alchemite™, a novel deep learning approach, and its application to optimising kinase profiling programmes. The…
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.
