Practical applications of matched series analysis
This paper, co-authored with our colleagues at NextMove Software, explores applications of Matched Series Analysis within StarDrop’s Nova module to…
Data analysis and structure-activity relationships
In discovery, whether your starting point is a compound structure, target or data on literature or proprietary compounds, the first step is to make sense of the information you already have.
Make sense of your structure-activity relationships
To pinpoint the best chemistry to explore, there are a variety of methods you can use for SAR analysis:
R-group analysis
Explore the effects that variations to R-groups or linkers can have on compound properties. Consider the impact of scaffold hopping, enumerate libraries to explore the properties of virtual compounds, and visualise findings with SAR tables and chemical spaces.
Clustering
Group similar compounds to identify chemical series within your data set, triage high throughput screening results and identify regions of chemistry with the right properties for your projects.
Matched pair analysis
Find pairs of molecules that differ by replacement of a single fragment to assess their impact on compound properties. Discover the transformations which provide significant improvements, and use these in your lead optimisation strategies.
Activity landscape analysis
From a network of similar compounds, highlight property differences to identify activity cliffs, regions of high variation in activity with interesting SAR, and regions of ‘flat’ SAR, which may present opportunities to optimise other properties without negatively impacting activity.
Data visualisation that works for you
When interpreting the results of your analyses, visualisation can be incredibly powerful. Beautiful interactive chemical structures, alongside charts, graphs, maps and more enable you to quickly and easily get an overview of the relationships in your data.
Having a range of data visualisation options at your fingertips is essential; everyone has a preference for different representations, while certain graphs and plots will be more useful in specific situations. Want to explore some of our options?
Card View
Break free from traditional spreadsheets and view compounds and their relationships in an intuitive, flexible way.
Glowing Molecule
Highlight the regions of your compounds which have the greatest influence on predicted properties to dynamically guide the design of high-quality compounds.
Charts and graphs
All visualisations in our StarDrop platform’s comprehensive range are interactively linked to one another and the underlying data, so you can explore relationships between structures and properties more easily.
3D visualisation
Harness interactive 3D visualisation and modelling to quickly understand the critical interactions driving potency and selectivity of your compounds to guide new compound designs.
Reveal transformative insights
Enhance your expertise with AI-guided discovery. In drug discovery, it’s too expensive to run all experiments on all interesting compounds, so we’re often dealing with very sparse data sets, covering lots of different chemistries and endpoints. Experimental errors and uncertainty within predictions can make it even more difficult to identify the best compounds for progression.
Avoid missed opportunities, pinpoint the best compounds, assays and experiments for your project, and go from concept to candidate faster, with proven AI methods.
Resources from across the site
Finding the rules for successful drug optimisation
Summary In this drug optimisation article, co-authored with Pfizer we discuss new ‘rule induction’ methods. These explore complex data to…
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