Machine Learning 101: How to train your first QSAR model
My hope is that these posts will be of interest to people who want to understand more of the nuts…
How does StarDrop compare to Semeta?
What are StarDrop and Semeta? Semeta is a tailored platform for DMPK scientists. It enables users to address key challenges…
How do I know if Optibrium’s predictive models work?
Data curation for model building A model can only be as good as the data it has been trained on.…
What’s the importance of cytochrome P450 metabolism?
Why focus on cytochrome P450 enzymes? CYPs are a ubiquitous superfamily of heme-containing monooxygenases responsible for approximately 70–80% of observed…
What other software does StarDrop integrate with?
StarDrop — A Swiss Army knife for drug discovery It’s designed to fit right in with the other tools you…
How do StarDrop and LiveDesign compare?
Finding the right fit There’s no one-size-fits-all solution when it comes to data visualisation and analysis tools for drug development…
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…
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…
The complexity of collaboration in drug discovery
Everyone knows smooth collaboration can speed up successful drug discovery projects. But how can we collaborate easily in drug discovery…
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?
Single-scaffold R-group analysis
In this quick example, we will look at a single-scaffold R-group analysis to identify any functionalities which are influencing potency. The data…
Predicting routes of Phase I and II metabolism based on quantum mechanics and machine learning
This peer-reviewed paper in Xenobiotica describes a new method to determine the most likely experimentally-observed routes of metabolism and metabolites based on our WhichP450™, regioselectivity and new WhichEnzyme™ model.
2D structure alignment
Using the 2D structure alignment tool in StarDrop, define a substructure to perform a rigid alignment of molecules in the data set.
Predicting regioselectivity of cytosolic SULT metabolism for drugs
This paper describes a model to predict whether a particular site on a molecule will be metabolised by cytosolic sulfotransferase enzymes (SULTs).
