synthetic accessibility
**Synthetic Accessibility** in chemistry AI refers to computational methods that estimate how difficult or easy it is to synthesize a given molecule in the laboratory, producing a synthetic accessibility score (SA score) that reflects the complexity of the required synthetic route, reagent availability, and number of synthesis steps. AI-based SA scoring is essential for prioritizing computationally designed molecules that can actually be made in practice.
**Why Synthetic Accessibility Matters in AI/ML:**
Synthetic accessibility is the **critical reality check for generative chemistry**—generative models can propose millions of novel molecules with desired properties, but only those that can be practically synthesized have value, making SA scoring essential for filtering computationally designed candidates.
• **Ertl SA Score** — The most widely used heuristic SA score (1-10 scale, 1=easy, 10=hard) combines fragment contributions (common fragments = easier) with complexity penalties (stereocenters, macrocycles, ring fusions = harder); fast to compute but limited in accuracy
• **Retrosynthesis-based scoring** — AI retrosynthesis tools (ASKCOS, IBM RXNMapper) attempt to find synthetic routes to target molecules; the number of steps, availability of starting materials, and route confidence provide a more realistic but computationally expensive SA assessment
• **ML-based SA models** — Graph neural networks and fingerprint-based models trained on databases of successfully synthesized molecules (e.g., USPTO reactions, patent literature) learn to predict synthesis difficulty, capturing patterns beyond simple heuristics
• **SCScore (Synthetic Complexity)** — A neural network trained on reaction data to predict relative synthetic complexity: the output of a reaction should be more complex than its inputs; SCScore provides a continuous complexity measure learned from actual chemical transformations
• **Integration with generative models** — SA scores serve as constraints or rewards in molecular generation: generative models penalize molecules with high SA scores, reinforcement learning uses SA as a reward component, and filtering removes synthetically intractable candidates
| Method | Basis | Score Range | Speed | Accuracy |
|--------|-------|------------|-------|----------|
| Ertl SA Score | Fragment heuristics | 1-10 | Very fast | Moderate |
| SCScore | Reaction data (NN) | 1-5 | Fast | Good |
| SYBA (SYnthetic BAyesian) | Bayesian scoring | Continuous | Fast | Good |
| Retrosynthesis (ASKCOS) | Route planning | Steps/confidence | Slow (seconds) | High |
| RAscore | Retrosynthesis feasibility | 0-1 probability | Fast | Good |
| Expert chemist | Domain knowledge | Subjective | Very slow | Highest |
**Synthetic accessibility scoring bridges the gap between computational molecular design and practical chemistry, ensuring that AI-generated drug candidates and materials can be translated from in silico predictions to real-world synthesis, providing the essential feasibility filter that makes generative chemistry actionable for drug discovery and materials development programs.**