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

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.