StarDrop Features
Data Visualisation
Generate interactive charts, chemical spaces and R-group analyses
StarDrop can instantly display a comprehensive range of visualisations. You can view multiple charts simultaneously, all of which are interactively linked to one another and the underlying data, enabling you to explore the relationships between multiple properties and compound structures.
Charts and Dashboards
StarDrop’s drug discovery data visualisation provides a wide variety of chart types, including
- Histograms
- 2D and 3D scatter plots
- Pie charts
- Box plots
- SAR and category plots
- Radar plots
- Heat maps
- Line and multi-line plots
- ROC curves
Multiple data sets and series can be included in the same chart, additional properties can be represented by colour, point, size and symbol, border or transparency, and charts can be trellised to highlight relationships between compounds, properties and data sets.
With StarDrop you can instantly zoom, pan and rotate your charts to spot important relationships. You can explore your data further by interactively selecting interesting compounds or filter the data in your charts by multiple properties. Compounds of interest can be annotated and labelled and you can also customise your charts with different fonts, ready for inclusion in your reports or publications by simple copy and paste.
With StarDrop’s ‘dashboard’ capability you can combine multiple interactive charts in a configurable project view, so that your team can track the progress of a project at a glance.
Chemical Space
StarDrop’s unique 2- and 3-dimensional chemical spaces enable you to easily explore the distribution of properties or scores across the diversity of chemistry in your project and consider the impact of different compound selection strategies.
- Multiple data sets: compare different chemical series and thousands of data points
- Flexible: create chemical space projections based on structure or properties
- Easy visualisation: set colour, symbol, size, border and transparency of points in the plot, according to properties or scores to identify ‘hot spots’ of high quality compounds and narrow down your choices quickly
- Selection: quickly and easily select sets of compounds with a balance of properties while spreading risk across different chemistries
- Get more detail: see a compound’s structure by hovering over the corresponding point and click for more detail or further analysis
Information-rich SAR Plots
With StarDrop you can create information-rich structure-activity relationship (SAR) plots, enabling you to visualise the impact of variations to R-groups, fragments or linkers on the properties of compounds within a chemical series.
StarDrop’s visualisation capabilities include the ability to display various chart types within each cell of an SAR plot including:
- Heat maps
- Radar plots
- 2D scatter plots
- Pie Charts
Additional properties can be represented by colour and size, and charts can be trellised to highlight relationships between compounds, properties and data sets.
Clustering
Cluster groups of ‘similar’ compounds together, for example to identify chemical series within a data set of diverse compounds, analyse SAR around hits for triaging results from high throughput screening or to identify ‘regions’ of chemistry that may yield good properties or scores.
StarDrop provides three different approaches to defining clusters:
- Maximum common substructure
- Compound similarity
- Properties
Substructure Search
With StarDrop you can easily search for compounds based on flexible substructure criteria, including the unique ability to specify variable linkers of defined length and cyclicity, as well as:
Variable atom criteria
- Element
- Charge
- Connectivity
- Number of hydrogens
- Arbitrary SMARTS
Variable bond criteria
- Bond order
- Aliphatic/aromatic
- Cyclic/acyclic
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Discover StarDrop™
With its comprehensive suite of integrated software, StarDrop™ delivers best-in-class in silico technologies within a highly visual and user-friendly interface. StarDrop™ enables a seamless flow from the latest data through predictive modelling to decision-making regarding the next round of synthesis and research, improving the speed, efficiency, and productivity of the discovery process.