synkit

name: synkit description: Use when working with SynKit for graph-based reaction informatics: ITS/MTG graph construction, reaction canonicalization, AAM validation, DPO rule extraction and composition, chemical reaction network (CRN) analysis, subgraph matching, and synthesis planning primitives.

Graph-based Python toolkit for reaction informatics: ITS graph construction, canonicalization, AAM validation, DPO rule extraction, CRN analysis, and synthesis planning.

Paper: doi:10.1021/acs.jcim.5c02123 | JCIM 2025

When to Use This Skill

Converting reaction SMILES → ITS graphs (NetworkX) → DPO rules (GML)
Validating or comparing atom-to-atom mappings (AAMValidator)
Canonicalizing reaction SMILES (CanonRSMI) or ITS graphs (GraphCanonicaliser)
Clustering reactions by structural similarity (WL graph hash)
Extracting and composing reaction rules (DPO formalism)
Analyzing chemical reaction networks (Feinberg deficiency theory, Petri nets)
Detecting autocatalysis, siphons, traps in reaction networks
Planning synthetic routes via rule composition (SynReactor)
Building reaction databases or curating USPTO/ChEMBL reaction data

Core Concept: ITS Graph

The Imaginary Transition State (ITS) graph merges reactant and product graphs into a single labeled multigraph:

Nodes: atoms with attributes (symbol, charge, radical, hybridization, H count)
Edges: bonds with attributes (bond type, change flag: formed / broken / unchanged)

ITS ≡ CGR (Condensed Graph of Reaction) — same structure, different naming tradition.

Quick Start

from synkit.IO import load_reaction_smiles
from synkit.Graph import ITSConstruction
from synkit.Chem import CanonRSMI, AAMValidator

# 1. Parse reaction SMILES → ITS graph (NetworkX)
rxn_smiles = "[CH3:1][OH:2].[Na:3][H:4]>>[CH3:1][O:2][Na:3].[H:4][H:5]"
its = ITSConstruction.from_reaction_smiles(rxn_smiles)

# 2. Canonicalize the reaction SMILES
canon = CanonRSMI(rxn_smiles).canonicalize()

# 3. Validate atom-atom mapping
valid = AAMValidator(rxn_smiles).is_valid()

Router — What to Read

Task	Reference
Load reactions, format conversion (SMILES ↔ ITS ↔ GML), data I/O	`references/io-conversion.md`
Canonicalization (CanonRSMI), AAM validation, Reaction class	`references/chem-standardization.md`
ITS construction, MTG, graph canonicalization, WL hashing, subgraph search	`references/graph-its.md`
DPO rules, GML format, rule composition, SynReactor forward/retro	`references/rule-dpo.md`
CRN building, Feinberg deficiency theory, Petri nets, autocatalysis	`references/crn-analysis.md`
Synthesis planning, route construction, pathway analysis	`references/synthesis-planning.md`

Key Submodules

Module	Role
`synkit.IO`	Reaction SMILES parsing, SMILES ↔ ITS ↔ GML conversion
`synkit.Chem`	`CanonRSMI`, `AAMValidator`, `Reaction` standardization
`synkit.Graph`	`ITSConstruction`, `GraphCanonicaliser`, WL hash, subgraph search
`synkit.Rule`	DPO rules, GML handling, rule composition
`synkit.Synthesis`	Forward/retro prediction, route exploration
`synkit.CRN`	CRN construction, Feinberg theory, Petri-net analysis
`synkit.Vis`	Reaction and mechanism visualization

Conversion Pipeline

Reaction SMILES (atom-mapped)
    │
    ▼ IO / Graph.ITSConstruction
ITS Graph (NetworkX)         ← cluster, hash, validate
    │
    ▼ Graph.GraphCanonicaliser
Canonical ITS Graph          ← canonical form independent of atom ordering
    │
    ▼ Rule module
DPO Rule (GML format)        ← compose, apply, store

All conversions are lossless for balanced, atom-mapped reactions. Caveats: stereochemistry is omitted; explicit H at reaction centers required for GML → SMILES reversion.

Installation

pip install synkit          # core (RDKit + NetworkX)
pip install synkit[all]     # full (+ transformers for RXNMapper)
# Python ≥ 3.11 required

Related Skills

rdkit — molecule preprocessing before SynKit ingestion
torchdrug — retrosynthesis with GNNs (complementary ML approach)
deepchem — molecular ML when rule-based approach insufficient