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Interactive Data Visualization
An overview of some of the visualization capabilities of the Datagrok platform, including the concepts of views, viewers, selection, filter, and layouts.
Scripting viewers are usually written in R, Python, or Julia. While not as interactive as the core Datagrok viewers, they allow to easily use thousands of visualizations already developed for these languages.
This is an overview of the cheminformatics capabilities available in the Datagrok platform. During the interactive sessions, we will do the following:
• import molecules in the SMILES format
• take a closer look at the molecule-specific info panels, including 3D structure, SDF, toxicity prediction, drug likeness, CHEMBL substructure and similarity, identifier conversion, PubChem integration, predictive models, as well as usuing community-produced scripts as context info panels
• sketch new molecules
• retrieve molecules by name, or by other identifiers
• perform in-memory substructure search
• perform similarity and diversity analyses using different metrics and fingerprints
• calculate descriptors
• perform R-Group analysis
• visualize molecules on a scatter plot, bar chart, grid, form, and tile viewer
• learn how to write RDKit-based Python scripts that take molecules as inputs
• search for substructures in a database powered by a chemical cartridge
• reproduce above-mentioned steps by applying recorded macros
We will learn how to create and share database connections, and interactively explore database content using a number of tools: • Context panel - for quick browsing • Visual query - for interactive aggregation and pivoting • Schema browser
Using formulas in calculated columns
Molecular similarity and diversity
Similar compounds have similar properties, so molecular similarity and molecular diversity techniques are important cheminformatics tools. In this video, we will learn how to interactively explore a dataset using these techniques.
In this video, we will learn how to interactively aggregate data using the Datagrok platform.
How do we choose the best location for a new coffee place, given the historical sales data? Datagrok to the rescue! In less than 20 minutes, we will achieve the following:
• retrieve historical data from the Postgres database
• explore, visualize, and clean it
• impute missing values
• extract census data from the long/lat coordinates
• perform multivariate analysis
• build multiple predictive models, and assess their performance
• build an interactive map for predicting sales
• deploy it as an app to all users in our company
In this video, we will join two tables by key columns, using different join types. Learn more
Compare data snapshots using the Table Comparer tool. Learn more