01
Check a pasted SMILES
Show a parse error before a structure drawing or descriptor calculation.
Result: A clear valid-or-invalid input result
Open task guideChoose the environment that fits the work you are doing. The same chemistry core is available in the browser, Python, Rust, and a local MCP process.
Each guide starts with an input, a short set of steps, and an expected result you can check yourself.
01
Show a parse error before a structure drawing or descriptor calculation.
Result: A clear valid-or-invalid input result
Open task guide02
Keep one input SMILES with calculated molecular weight, LogP, and TPSA.
Result: A traceable first-pass property record
Open task guide03
Use one fingerprint setting to retain a repeatable local Tanimoto score.
Result: A documented ECFP4 comparison
Open task guide04
Keep each input row with calculated properties or a parse error before filtering.
Result: A row-preserving local property report
Open task guide05
Parse one SMILES and print atom count, molecular weight, and TPSA in the same native process.
Result: A local Rust command-line result
Open task guide06
Send one JSON-RPC request to chematic-mcp over stdio before registering it with an AI client.
Result: A verified local structured response
Open task guide07
Store one complete SMILES beside its calculated molecular formula and molecular weight.
Result: A compact, traceable molecule record
Open task guide08
Return chematic's canonical SMILES locally and retain it with the exact input and version.
Result: A versioned canonical-SMILES record
Open task guide09
Keep all source rows while using local canonical SMILES to flag candidates for review.
Result: A row-preserving duplicate-review report
Open task guide10
Preserve source SMILES while generating a separate, policy-defined representation for comparison.
Result: A source-preserving standardization report
Open task guide11
Classify every source value before a batch API can skip invalid input.
Result: An input-order-preserving preflight report
Open task guide12
Write a scalable local structure file while retaining the exact source SMILES.
Result: A traceable SVG structure artifact
Open task guide13
Write a scalable local reaction diagram from an explicit reaction SMILES record.
Result: A traceable SVG reaction artifact
Open task guide14
Keep a structure name and exact source SMILES in one local SDF record.
Result: A traceable local SDF artifact
Open task guide15
Write valid CSV rows as SDF records while preserving every source row and parse error in a report.
Result: A traceable SDF library and conversion report
Open task guideTry molecular analysis locally in a browser with no server round trip.
Open the browser guide 02Use notebooks, pandas workflows, and batch analysis from PyPI.
Open the Python guide 03Build native services, command-line tools, and embedded chemistry workflows.
Open the Rust guide 04Give a local AI client structured chemistry tools through chematic-mcp.
Open the AI agents guide01
Best for interactive tools, offline explorers, and analysis that should stay in the browser.
npm install @kent-tokyo/chematicTry the browser playground Open the full guide02
Best for notebooks, DataFrames, and batch jobs over structure files.
pip install chematicOpen the quickstart notebook Open the full guide03
Best for native applications and services where the chemistry logic belongs in the same process.
cargo add chematic --features "smiles,perception,chem,3d,fp"Read the Rust API reference Open the full guide04
Best when an AI client needs typed chemistry operations on a local machine.
chematic-mcpRead the MCP guide Open the full guideNot sure where to start?
For a first look, open the browser playground. Use Python for notebooks and data work, Rust for applications and services, and the MCP tool when a local assistant needs chemistry operations.