AiZynthFinder

AiZynthFinder plans retrosynthesis routes, breaking a target molecule down into commercially available starting materials so you can judge synthetic accessibility. Reach for it once you have a candidate worth making — a designed hit or a virtual-screening pick — to scout routes before committing to synthesis or procurement. It is the natural next step after a generator like GenMol or REINVENT proposes new molecules. See what you can achieve for the full set.

What AiZynthFinder provides

AiZynthFinder is an open-source AI-powered retrosynthesis planning tool developed by AstraZeneca's Molecular AI team. First released in 2020, it has been continuously improved based on 3+ years of industrial application. The latest AiZynthFinder 4.0 (published May 2024 in Journal of Cheminformatics) includes significant feature enhancements based on real-world medicinal chemistry feedback.

Citations

Genheden S, Thakkar A, Chadimová V, Reymond JL, Engkvist O, Bjerrum E. AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning. Journal of Cheminformatics. 2020 Nov 17;12:70. doi: 10.1186/s13321-020-00472-1

Saigiridharan L, Hassen AK, Lai H, Torren-Peraire P, Engkvist O, Genheden S. AiZynthFinder 4.0: developments based on learnings from 3 years of industrial application. Journal of Cheminformatics. 2024 May 23;16:57. doi: 10.1186/s13321-024-00860-x

Performance and industrial validation

• 80-86% success rate for finding routes where all starting materials are in stock (tested on AstraZeneca designs and REINVENT-generated compounds)
• 71% success rate on ChEMBL compound targets
• Used routinely by ~30 medicinal chemists at AstraZeneca
• Documented impact on multiple drug discovery programs

Core capabilities

• Template-based retrosynthesis: Applies thousands of reaction templates trained on patent and literature data to break down target molecules into commercially available starting materials.
• Multi-policy scoring: Uses different neural network policies (expansion policies, filter policies) to score and rank synthetic routes based on template probability and route feasibility.
• Stock-aware planning: Checks route feasibility against chemical inventories (the bundled ZINC stock, or a custom library you upload) to ensure purchasable starting materials.
• Route clustering and filtering: Groups similar routes and applies filters to remove duplicates and low-quality pathways.
• Search algorithm: The platform runs AiZynthFinder's Monte Carlo Tree Search (MCTS); the library also supports other search strategies, but MCTS is the one configured here.

New in AiZynthFinder 4.0 (2024)

• Filter policies: Apply learned filters to discard unlikely or undesirable reactions
• Universal one-step support: Works with any one-step retrosynthesis model
• Scoring framework: Flexible scoring of routes with customizable metrics
• Human-guided synthesis planning: Prompting system for incorporating chemist knowledge (2025)
• Bonds-to-freeze constraints: Specify bonds that should not be modified during retrosynthesis

Key parameters

Input molecule

• SMILES: Target molecule in SMILES format (required)
• File input: Support for SMILES files with one molecule per line

Search configuration

• Policies: The bundled expansion policy is uspto, a template-based model trained on USPTO reaction data
• Stocks: Chemical inventories to check against, e.g. zinc
• Filters: Route filters to apply during search — strict, recommender, or all, in increasing order of permissiveness

Search limits

• Time limits: Maximum search time to prevent runaway computation
• Route limits: Maximum number of routes to generate and return
• Depth limits: Maximum number of reaction steps in any route

When to use it

Primary use cases

• Lead optimization: Generate synthetic routes for hit-to-lead compounds to assess synthetic accessibility early in drug discovery
• Route scouting: Compare multiple synthetic strategies for the same target molecule
• CRO preparation: Generate detailed route proposals with starting material costs for contract research organizations
• Library design: Evaluate synthetic feasibility of virtual screening hits before ordering

Best paired with

• ADMET-AI: Score synthetic intermediates for drug-like properties
• Molecular docking: Validate binding poses of proposed intermediates
• Cost analysis: Estimate total synthesis costs including reagent prices

Guardrails and limits

Input constraints

• Single target only: Designed for one molecule per job; use batch processing for multiple targets
• Organic molecules: Optimized for small organic compounds (<1000 Da); peptides and biologics not supported
• Valid SMILES: Requires well-formed, canonical SMILES strings

Stock requirements

• At least one stock source: Must specify at least one chemical inventory (the bundled ZINC stock, or a custom library)
• Stock availability affects results: Routes are only marked "purchasable" if all starting materials exist in specified stocks
• Custom stocks: Can upload your own building block libraries in SMILES format

Performance limits

• Search time: Typically 5-30 minutes depending on molecule complexity and search breadth
• Route output: Limited to top 50-200 routes to prevent overwhelming results
• Memory usage: Requires significant RAM for large template databases

Computational scaling

• Molecule complexity: Search time increases exponentially with molecular size and complexity
• Stock size: Larger stock databases improve route finding but slow search
• Filter selection: A more permissive filter (all) surfaces more routes but includes lower-quality ones; strict runs faster and cleaner

Best practices

Input preparation

• Standardize SMILES: Use canonical SMILES and ensure proper atom valence
• Remove counterions: Strip salts and counterions that aren't part of the core structure
• Check connectivity: Ensure molecules are properly connected (no disconnected fragments)

Stock selection

• Match procurement reality: Choose stocks that reflect your actual purchasing options
• Combine sources: Use multiple stock sources for broader coverage
• Update regularly: Refresh custom stocks as your inventory changes

Search strategy

• Start conservative: Begin with default settings, then expand search if needed
• Filter selection: Start with recommender for a balance of speed and route quality
• Filter judiciously: Apply filters to reduce noise but don't over-filter

Result interpretation

• Route scores: Higher scores indicate more likely synthetic routes
• Purchasability: Focus on routes marked as purchasable in your selected stocks
• Route diversity: Review multiple routes to understand synthetic options

Troubleshooting common issues

• No routes found: Check stock selection and molecule validity
• All routes non-purchasable: Your target may require custom synthesis of intermediates
• Search timeout: Reduce search breadth or use simpler policy
• Invalid SMILES: Validate input SMILES using cheminformatics tools

Chat Usage

Example Prompts

• "Plan a retrosynthetic route for CC(=O)Oc1ccccc1C(=O)O" (aspirin)
• "Run AiZynthFinder on my top 3 REINVENT hits"
• "Find the most efficient synthesis route for ibuprofen with all reagents in stock"

Workflow Chaining

• Feeds into: ZINC (purchasability and vendor catalog codes for building blocks), Compound Passport (synthesis section)
• Accepts from: PubChem (compound SMILES), REINVENT/GenMol (generated molecules), ChEMBL (hit compounds)

FAQ

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