---
title: "Cheminformatics overview"
description: "Turn SMILES into descriptors with RDKit, standardize structures, and classify or fingerprint reactions before the heavier models run."
tags:
  - section
  - cheminformatics
generated: true
---

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](/docs/plans-and-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](/docs/capabilities).

## Tools in this section

| Tool | What it does |
|---|---|
| [RDKit Toolkit](/docs/rdkit) | 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](/docs/rxnfp) | Generate reaction fingerprints and search a reference reaction index by similarity with an RXNBERT transformer model. |
| [Reaction Classifier](/docs/reaction-classifier) | Classify chemical reactions and suggest conditions using Rxn-INSIGHT with RXNFP fallback. |
| [Biotite](/docs/biotite) | Python toolkit for sequence and structure bioinformatics on the platform. |

## Read next