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…
How can I make the most of my predictive models for drug discovery?
What’s the purpose of a predictive model? What’s the value of predictive models for drug discovery? Most of the undergraduate…
How does generative chemistry work, and how can it help me?
The role of generative chemistry in drug discovery A key difficulty in finding new drugs is the sheer size of…
Which is the best metabolite prediction software?
Discover which metabolite prediction software is best for your needs in this comprehensive guide from Optibrium. Compare top tools like Meteor Nexus, MetaSite, and StarDrop to make informed decisions for drug metabolism prediction
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.
How much does drug discovery software cost?
How number of users affect drug discovery software costs The number of people who need access to the platform is…
Optibrium’s quantum mechanics and machine learning methods predict routes of drug metabolism
Peer-reviewed study published in Xenobiotica describes an innovative new method that predicts the routes and products of Phase I and II metabolism with high sensitivity and greater precision than
other approaches
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…
How to build a better QSAR model
To guide drug design, it’s important to understand the likely ADME and physicochemical properties of your compounds at an early…
Mastering multi-parameter optimisation
Develop advanced MPO strategies and target the right compounds, faster.
We’re diving back into our favourite subject: multi-parameter optimisation.
Finding balance in drug discovery through multi-parameter optimisation
Successful drugs require a delicate balance of many properties, such as potency, ADME and toxicity, to meet a project’s therapeutic objective. To make decisions about compound progression and assay selection, the available data must be assessed against project-specific criteria. However, the data on which we base our decisions often come from different sources and can vary in quality, so how can we use this information to make confident decisions? In addition, how can we be sure that the criteria we’re using are the most appropriate?
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.
Predicting reactivity to drug metabolism: beyond CYPs
Introduction Predicting sites of metabolism (SoM) enable chemists to be more efficient in optimising the structure of new chemical entities…
Multi-parameter optimisation in practice
This webinar describes example applications of multi-parameter optimisation to find high-quality lead compounds.
N- and S-oxidation model of the flavin-containing monooxygenases
Introduction Existing computational models of drug metabolism are heavily focused on predicting oxidation by cytochrome P450 (CYP) enzymes, because of…
Compound prioritisation and selection
In this demo we’re going to take a look at how StarDrop can guide the prioritisation and selections of compounds using a combination of in vitro and in silico data.
