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.
RDKit turns SMILES into molecular descriptors, fingerprints, and drug-likeness filters, the fast, deterministic groundwork that prepares and interprets the inputs and outputs of heavier models. Reach for it whenever you need Lipinski or PAINS flags, similarity fingerprints, or format conversions without invoking an ML model. It complements synthesis planning in AiZynthFinder by profiling the molecules you route. See what you can achieve for the full set.
RDKit is the most widely used open-source toolkit for cheminformatics, developed by Greg Landrum with extensive community contributions. It provides a fast C++ backend with a versatile Python API, supporting molecular parsing, fingerprinting, descriptor calculation, substructure searching, and 3D conformer generation.
Citations & Resources
Landrum G, et al. RDKit: Open-source cheminformatics software. https://www.rdkit.org
Documentation: https://www.rdkit.org/docs/
GitHub: https://github.com/rdkit/rdkit
Key capabilities
- Molecular I/O: Read/write SMILES, InChI, MOL, SDF, and PDB formats
- Descriptors: Calculate molecular properties for QSAR modeling
- Fingerprints: Morgan, RDKit, Atom Pair, and MACCS fingerprints
- Database cartridge: PostgreSQL integration for substructure/similarity searches
- Version:
rdkit>=2023.9.1(pip)
RDKit on the platform covers fingerprinting, similarity, conformer generation, and cross-format conversions. Use the pages linked above for task-focused guides and copy-pasteable code.
Where RDKit runs
RDKit runs on the platform's servers, not in your browser and not on a GPU.
- In your chat's sandbox. Descriptors, fingerprints, similarity, substructure search, standardization, drug-likeness filters and Murcko scaffolds on up to 200 molecules (100 for similarity) run inside your chat's sandbox, a container on AWS ECS Fargate that belongs to that chat alone. The result card completes in the same turn.
- As a CPU job. 3D conformers, MCS, diverse picking, CSV or SDF exports, and larger batches run as an
rdkit_fulljob on a CPU worker. If the chat has no active sandbox, the lightweight operations fall back to this job too. - In your own code. RDKit is preinstalled in the sandbox, so you can also ask Ivy to run your own RDKit Python against files in your workspace.
The 2D structure previews drawn in chat use RDKit.js in the browser, for display only; no analysis runs there.
Chat Usage
Example Prompts
- "Calculate RDKit descriptors for aspirin"
- "Run RDKit similarity analysis on my compound set"
- "Compute molecular fingerprints for these 10 SMILES"
Workflow Chaining
- Feeds into: Compound Passport (molecular properties section), ADMET screening
- Accepts from: PubChem/ZINC/ChEMBL (SMILES), REINVENT/GenMol (generated molecules)
FAQ
Read next
RXNFP
Generate reaction fingerprints and run similarity searches with RXNBERT transformer models.
Reaction Classifier
Classify chemical reactions and suggest conditions using Rxn-INSIGHT with RXNFP fallback.
Which tool?
Match a scientific goal to the tool that answers it.
Workflows
Guided pipelines and copy-and-adapt prompt recipes for common goals.
Was this page helpful?
Cheminformatics overview
Turn SMILES into descriptors with RDKit, standardize structures, and classify or fingerprint reactions before the heavier models run.
Conformers & Minimization
Embed conformers with ETKDGv3, minimize under MMFF94s or fall back to UFF, pick the lowest-energy conformer from stored energies, and write them to an SDF.