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…
Which ADMET properties are important for me to predict?
How can I predict my compound’s absorption? The first of the ADMET properties relate to absorption. Understanding how a drug…
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 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.
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
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
Ligand-based drug design using Surflex eSim 3D
This worked example uses StarDrop’s Surflex eSim3D module to assess a small library of compounds for their similarity to known Heat Shock Protein 90 (HSP90) ligands.
Automatic generation of new compound ideas
During this example we will consider three compounds from a lead series which we would like to try to evolve into a candidate. The compound has a good profile of ADME properties but insufficient inhibition of the target, the Serotonin transporter. In this example we will use StarDrop’s Nova module to generate new ideas for compounds to improve the potency while maintaining the balance of other properties.
Fast follower: optimising P450 metabolic stability
In this example we will explore the feasibility of pursuing a fast-follower for Buspirone, a 5-HT1A ligand used as an anti-anxiolytic therapeutic, which has a known liability due to rapid metabolism by CYP3A4.
Guided multi-parameter optimisation of 2D and 3D SAR
This SeeSAR and ADME QSAR worked example uses a combination of 2D and 3D methods to understand and optimise a virtual library of Heat Shock Protein 90 (HSP90) inhibitors.
Automatic QSAR model building and validation
This example explores the application of the Auto-Modeller module to build a QSAR model of potency against the Muscurinic Acetylcholine M5 receptor, based on public domain Ki data. The resulting model is applied to novel compound to predict their properties and visualise the SAR.
Building better QSAR models: A new framework for consistent performance across diverse prediction tasks
Accurate QSAR models lead to more efficient and cost-effective molecular discovery. Better predictions enable you to prioritise the optimal compounds…
