How is AI transforming drug discovery chemistry?
Imagine you’re trying to find the correct key to unlock a treasure box, but there are billions of keys to…
Machine learning 101: How to build your first neural network
What are neural networks? Neural networks (NNs) in various forms are very common nowadays, and specific architectures are used for…
How much do AI drug discovery platforms cost?
Which AI platform do you need? The first thing you’ll need to do is decide what you need to achieve…
How should I prepare and store my data for cheminformatics applications?
Structuring your cheminformatics data First, the easiest format to work with is a simple table of data, where each row…
How can I model large molecules like macrocycles?
What comprises large molecules? When we talk about “large molecules,” we often think of biologics like monoclonal antibodies, proteins, and…
Data integration in AI-guided drug discovery
Benefits of continuous integration to use up-to-date data in model building Cerella has the ability to work with and learn…
How do I get budget approval for drug discovery software?
Buying software for your company can be a challenge. Every organisation does things differently, and there is often no handbook…
Is AI improving drug discovery?
Is AI-guided drug discovery faster and cheaper? The evidence for this is, by definition, anecdotal. No one runs the same…
What’s the difference between QSAR and imputation predictive models – which method should I use and when?
We’re often asked, “What’s the difference between QSAR and imputation models?”, so I’m going to explain how the methods differ, their advantages and disadvantages, and when each approach is applicable.
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.
7th RSC-BMCS AI in chemistry conference
The Chemical Information & Computer Applications Group (CICAG) and Biological & Medicinal Chemistry Sector (BMCS) of the Royal Society of Chemistry are once again organising a conference to present the current advances in AI and machine learning in Chemistry.
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.
Optibrium partners with FMC Corporation to transform agrochemical discovery
CAMBRIDGE, UK and CAMBRIDGE, US 14 May 2024 – Optibrium, a leading developer of software and AI solutions for drug…
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.
