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).
Integrated prediction of Phase I and II metabolism
Watch Optibrium CEO Matt Segall and Principal Scientist Mario Öeren as they explore groundbreaking new quantum mechanics and machine learning models which go beyond P450s and provide insights on a broad range of enzymes involved in drug metabolism.
Heat maps in StarDrop
In StarDrop you can display heat maps for the properties contributing to an MPO score. We’ve extended this capability by…
Predicting regioselectivity of AO, CYP, FMO and UGT metabolism using quantum mechanical simulations and machine learning
This paper describes the prediction of the regioselectivity of metabolism by AOs, FMOs and UGTs for humans and CYPs for three preclinical species.
Matched pairs neighbourhood analysis
This short video gives an introduction to the Matched Pairs Neighbourhood tool in StarDrop’s Card View. If you are interested…
Predicting pKa using a combination of quantum mechanical and machine learning methods
This study aimed to create a model for predicting pKa using a semi-empirical quantum mechanics (QM) approach combined with machine learning (ML).
R-group decomposition
StarDrop’s R-group analysis makes it quick and easy to explore the variation of properties by the substituents within a chemical series. This…
Introduction to Card View designer
This short video illustrates how to you can create cards of any dimensions and lay out your data in a…
Matched Series for SAR Analysis
Try Matched Series Analysis in this follow-along tutorial
R-group matched pairs
This short video illustrates how to perform Matched Molecular Pair Analysis (MMPA) within a chemical series using StarDrop’s R-group analysis tool. This…
Introduction to matched series analysis
This short movie gives an introduction to StarDrop’s Matched Series Analysis which is part of the Nova module which searches databases of…
StarDrop summary analysis
StarDrop’s summary analysis tool enables you to quickly see trends across the properties in your data sets. Take a look…
Breaking free from chemical spreadsheets
This article explores the benefits of a more intuitive and flexible approach to viewing and interacting with drug discovery data,…
