Solutions
Domains
Datagrok core is data-agnostic. Using plugins, you can customize the platform for any domain of knowledge. For example:
- Automatic recognition of domain-specific data types, e.g., molecules
- Highly customized data rendering
- Fast, data-aware spreadsheets and visualizations
- Specialized data editing and filtering interfaces, e.g., substructure filters
- Domain-specific calculation and data processing functions
- Fit-for-purpose apps
- Cheminformatics
- Bioinformatics
- Data science
- Scientific computations
- NLP
- Automatic detection of chemical structures upon data import. Support for multiple formats
- Highly customized 2D (RDKit or OpenChemLib) and 3D (NGL) rendering of molecules
- Multiple molecular sketchers
- Powerful, chemical spreadsheet and other chemically-aware viewers
- Customizable chemical info panes
- Substructure search
- Chemical space analysis
- R-groups decomposition
- Scaffold tree
- Activity cliffs
- Matched molecular pairs
- Dose-response curves
- ADMETox
- Comprehensive ML toolkit for clustering and other tasks, dimensionality reduction using WebGPU. Built-in statistics
- And much more ...
- Automatic detection of sequences upon data import. Support for multiple formats. Handles nucleotides, natural and non-natural peptides, 3D-structures, and other modalities
- Powerful spreadsheet that shows both macro- and small molecules, domain-specific viewers
- Customizable biological info panes
- Sequence editing, search, and filtering
- Multiple sequence alignment
- Sequence composition
- Sequence space
- Sequence-activity relationship. Activity cliffs. SAR for peptides
- Oligonucleotides toolkit
- Connection to chemistry level: split to monomers, get the atomic-level structure. Monomer libraries management
- Comprehensive ML toolkit for clustering and other tasks, dimensionality reduction using WebGPU. Built-in statistics
- And much more ...
- Support for all major data science tasks, from data preparation to PCA/PCL
- Exploratory data analysis with built-in statistics
- Integration with Jupyter Notebook
- Predictive modeling
- Dimensionality reduction using WebGPU
- High-performance computing
- Custom computational scripts and algorithms in any language
- Auto-generated, interactive UI for computational models. Interactive widgets designed specifically for scientific apps
- Differential equation modeling for bioreactors and other complex processes
- Tools for advanced text analysis, including tokenization, sentiment analysis, and entity recognition
- Automatic detection of the text language
- Text summarization
- Keyword and relationship extraction
- Integration with external NLP libraries
Enterprise IT
- Seamless integration with any machine-readable data source
- Robust security features, including role-based access control, data encryption, and audit trails. Data catalog
- Collaboration features with version control across projects
- Scales well
- Extensive customization options through plugins
- Real-time data processing and analytics. Usage analysis
- Flexible deployment options: cloud-based, on-premises, hybrid