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…
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…
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.
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 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…
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?
Optibrium demonstrates accelerated lead optimisation in complex agrochemical development
Optibrium’s QuanSA 3D-QSAR method uses an active learning approach to successfully and more efficiently identify a mimic of a macrocyclic natural product
38th ACS National Medicinal Chemistry Symposium
Join Optibrium’s Chris Khoury at the 38th NMCS meeting in Seattle, 23-26 June
From UK-2A to florylpicoxamid: active learning to identify a mimic of a macrocyclic natural product
Scaffold replacement as part of an optimisation process that requires maintenance of potency, desirable biodistribution, metabolic stability, and considerations of synthesis at very large scale is a complex challenge.
How to run a successful drug discovery team
Watch industry leaders from Novartis, Apollo Therapeutics and Eikon Therapeutics as they discuss their highs and lows, experience and advice,…
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…
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.
Enamine plug-in for StarDrop
This script enables you to search EnamineStore, a database of commercially available compounds. This will return details about the availability of…
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.
Complex peptide macrocycle optimisation: combining NMR restraints with conformational analysis to guide structure-based and ligand-based design
Systematic optimisation of large macrocyclic peptide ligands is a serious challenge. Here, we describe an approach for lead optimisation using the PD-1/PD-L1 system as a retrospective example of moving from initial lead compound to clinical candidate.
