grit is enterprise-level data management made accessible to everyone. Flexible, extensible, and easy to get started with.
The open source platform allows you to store, manage, and visualise your data across all pre-clinical drug discovery phases. Structured, accessible, and ready to be analysed, retrieved, and used in AI algorithms.
In grit, you can store your all your modalities alongside your pre-clinical assay data from both in vitro, in vivo, DMPK, and toxicological data in SEND format.
Your science. Your data. Your rules.
Just tried out the newly open sourced “grit”. Super easy install and easy enough to figure out (I.e. I only had to look in the manual a few times to get set up).
Will definitely try out the plotting and SAR features (…)
Lucy Kate Ladefoged Thøgersen
Director of AI-driven drug discovery, Raven biosciencesDespite not having a background in computational chemistry or data science, installing and running the grit42 open-source platform from my laptop proved very straightforward when following the provided guide.
Additionally, the team at grit42 has been very responsive to any questions from our research group.
Anders E. Kiib
Postdoc at Aarhus UniversityWe do research data management the simple way. The platform is a no-code, ready-to-use product delivered as open source under a GPL license.
To get started, you configure the required metadata on the modalities you want to store, configure the relevant assay parameters, and then you’re ready to start loading data.
You can easily filter assays and compounds based on metadata to get the overview of your data you need for analysis.
The platform contains Data Tables - a SAR-table inspired tool to support data analysis.
Data Tables let you aggregate result data from your published experiments based on selected entities (Compounds, Batches, or even items from a Vocabulary) whether they have a structure or not.
Pick the entities you want to see, pick the assay columns you want to see and define how to aggregate values. You can now analyse your data in the table and with scatter plots, and export it to use it in external analysis tools.
Configure for different modalities (small molecules, oligos, antibodies, etc)
Structure > Compound > Batches registration
Controlled vocabularies
Support synonyms & combinations
Drag'n'drop assay data upload
Plot, visualise or export for external analysis
Data Tables (SAR)
Drug discovery project manager overview (upcoming functionality)
We've answered some of the most frequently asked questions here.
Any other burning questions?
Don't hesitate to send Marvin a message at marvin@grit42.com.
That’s easy. Either you go to GitHub and grab the code, or you follow the instructions in the README file on GitHub to download the Docker image and run the full platform in Docker.
The platform was originally developed by grit42, a Copenhagen-based software company built on +25 years of experience in managing data and developing software for drug discovery data management. We continue to improve and add functionality to the platform.
grit is published under a GPL license, which basically means that you can use it and do with it what you please - also if you are a company.
However, if you consider distributing it to others, certain responsibilities apply. Read more about GPL here
In general, we use GitHub for that, so go there to report bugs, suggest features, start a discussion, etc.
If you’re interested in a service contract for support, reach out to us at contact@grit42.com and let’s talk about how we can help.
Yes, absolutely. You - or an admin in your organisation - can define your own “modalities” in the platform and what metadata to store on them.
So, as an example, if you work with oligos, you can add a new modality (compound type) called oligo and fully configure the data columns you want to store in the platform on each oligo. The platform can handle several different modalities simultaneously.
Yes. Then you just ignore the assay/experiment part.
If you work with small molecules, you will get a fully functional compound database with structure viewer, exact match and substructure searching (via RDKit), and other filtering options as well as the ability to export subsets for further analysis.
The assay part of the platform can also be used to define in silico “assays”, so if you run prediction models, you can load the data into the platform, linked to the relevant compounds/structures, as well.
You can upload and store not just the small molecule structures (we use RDKit) and compound-level metadata, but also the individual batch-specific information and thereby ensure the result data are linked to the relevant, used batch.
The platform also supports compound-level synonyms, as well as supports combination therapy whereby two (or more) compounds are combined and registered/tested as a new compound.
Some experimental results are fairly simple tabular data, which the platform naturally support. It can also handle more complex data types, like in vivo animal-level data with dosing groups and other types of metadata about the study.
Users can add visualisations of the data in the platform out of the box (we use the plotly library) by selecting from a dropdown, or for more advanced visualisations, equipment data, or specific workflows, use one of our focused applications.
Controlled vocabularies enable platform-wide controlled terms and their accepted values. The vocabularies are a prerequisite for adding controlled assay metadata to the assay model and are essential for following FAIR principles.
