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.…
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…
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…
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.
26th North American ISSX and JSSX Meeting
The joint ISSX/JSSX meeting is for researchers looking to gain a deeper understanding of drug metabolism and pharmacokinetics.
38th ACS National Medicinal Chemistry Symposium
Join Optibrium’s Chris Khoury at the 38th NMCS meeting in Seattle, 23-26 June
Overcoming challenges in drug metabolism: in silico approaches
Interpreting metabolite-ID experiments; determining the right species for animal studies; providing optimisation suggestions for your medicinal chemistry colleagues to overcome…
Transferable machine learning interatomic potential for bond dissociation energy prediction of drug-like molecules
Predicting metabolism at an early stage is important in maximising the chance of a drug’s success. However, accurate, useful models…
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
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.
Optibrium releases powerful metabolism prediction capability in next generation StarDrop software
Backed by six years’ research, the new StarDrop Metabolism module combines quantum mechanics and machine learning to better predict the metabolic fate of drug candidates.
