Cheminformatics overview
Turn SMILES into descriptors with RDKit, standardize structures, and classify or fingerprint reactions before the heavier models run.
The connective tissue of small-molecule work: turn SMILES into descriptors, standardize structures, classify reactions, and compute the properties that filter and rank compounds. These tools rarely stand alone — they prepare and interpret the inputs and outputs of the heavier models.
Availability
These tools are metered by credits and gated by your plan — plans set your monthly credits, available model families, and which modal-app tools you can run. See Plans & tiers.
What you can achieve here
- Compute molecular descriptors and drug-likeness filters
- Standardize and manipulate SMILES
- Classify and fingerprint reactions
For which tool fits each, see What you can achieve.
Tools in this section
| Tool | What it does |
|---|---|
| RDKit Toolkit | Run RDKit descriptors, fingerprints, Lipinski and PAINS filters and Murcko scaffolds — in the sandbox up to 200 molecules, or as an rdkit_full job. |
| RXNFP | Generate reaction fingerprints and search a reference reaction index by similarity with an RXNBERT transformer model. |
| Reaction Classifier | Classify chemical reactions and suggest conditions using Rxn-INSIGHT with RXNFP fallback. |
| Biotite | Python toolkit for sequence and structure bioinformatics on the platform. |
Read next
What you can achieve
The outcomes these tools deliver — and which to pick when several do the same thing.
Which tool?
Match a scientific goal to a tool across every domain.
Concept clusters
How tools, jobs, and results fit together.
Workflows
Guided pipelines and copy-and-adapt prompt recipes for common goals.
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