Synthetic Accessibility: Definition, Importance, and How to Assess It with Neurosnap

What Is Synthetic Accessibility

Synthetic Accessibility (SA) refers to how easy or difficult it is to actually make (i.e. synthesize) a given small molecule in the lab, given the limitations of synthetic chemistry (available building blocks, reaction types, stereochemistry, complex scaffolds, etc.). It is a practical metric: a molecule may be promising in silico (activity, binding, ADMET predictions, etc.), but if it is too hard to make, that can block progress.

A commonly used SA scoring method is that of Ertl & Schuffenhauer (2009), which assigns an SA score from 1 (“very easy to synthesize”) to 10 (“very difficult”). This system combines two main contributions:

• Fragment contributions: how common or rare are the molecular fragments (substructures) in known synthesized compounds (from databases such as PubChem). Common fragments make synthesis easier. (BioMed Central)
• Complexity penalty: factors like molecular size, presence of large or fused rings, stereocenters, unusual ring types, symmetry, etc., which make synthesis more challenging. (BioMed Central)

Often, SA is treated as a continuous score (not just easy vs hard), to allow ranking among candidates. (SpringerLink)

Why Synthetic Accessibility Matters

In small molecule biology and drug discovery, synthetic accessibility matters for several reasons:

1. Feasibility and Cost If a molecule is very difficult to synthesize, the cost in time, reagents, labor, purification, etc., can be prohibitive. Projects with many hard‐to‐synthesize leads may stall due to synthetic bottlenecks.

2. Throughput & Iteration Drug discovery is iterative: you design or screen molecules, test them, then refine. If synthetic difficulties reduce the rate at which molecules can be made, this slows down the cycle of hypothesis → synthesis → testing → optimization.

3. Scale and Manufacturability Even when a small‐scale synthesis is possible, difficulties may multiply when scaling up (batch consistency, yields, reaction complexity, cost of starting materials). What is feasible at milligram scale may not be at gram or kilogram scale.

4. Integration with ADMET / Toxicity / Cost Trade-offs Often, small molecule design needs to optimize many factors: potency, selectivity, toxicity, solubility, stability, pharmacokinetics, and synthetic accessibility. A highly potent molecule that is nearly impossible to make is less useful than a somewhat less potent one that can be made reliably and affordably.

5. Generative Design & Computational Filtering In modern workflows, many molecules are proposed by computational tools or generative AI. Without synthetic accessibility filtering or estimation, many proposed compounds may never be synthesizable. Including SA metrics improves “realism” of proposed molecules. (BioMed Central)

6. Risk Mitigation Early assessment of synthetic difficulty helps avoid wasted investment (time, money) in molecules that later prove impractical. It allows prioritization of molecules not only for biological promise but also for manufacturability and synthetic risk.

How Synthetic Accessibility Is Determined / Estimated

Since assessing actual synthetic ease in wet‐lab is expensive, computational proxies are used. Some common methods / considerations:

• Fragment / Substructure Frequencies: How often fragments appear in known compounds (databases). More frequent fragments tend to indicate easier availability of building blocks and reaction precedents. (As in Ertl & Schuffenhauer’s SA score.) (BioMed Central)

• Molecular Complexity Metrics: Number of atoms (especially heavy atoms), molecular weight, ring complexity (size, fused/rings, bridgehead, spiro centers), stereochemistry, number of functional groups, unusual bond types (double, triple, aromatic, etc.).

• Topological and Graph Descriptors: Including measures of branching, connectivity, presence of heteroatoms, presence of strained rings, etc.

• Descriptors of Synthetic Strain / Unusual Features: E.g. high sp^3 carbon content, chiral centers, complex or rare scaffolds, non‐standard ring systems.

• Symmetry, redundancy, ease of assembling subunits: More symmetric or modular molecules tend to be easier, because parts can be reused or simpler routes may be found.

• Retrosynthetic Modeling: More advanced methods try to propose actual synthetic routes backward from the target, using known reaction data to see whether a plausible route exists. These take more compute/time but can provide stronger evidence. (Iktos)

• Empirical / Expert Judgement: Medicinal chemists’ experience remains a strong baseline; computational scores are often benchmarked against expert assessments. (BioMed Central)

A widely used computational implementation is RDKit’s sascorer.py, based on Ertl & Schuffenhauer’s work. (GitHub)

How to Determine Synthetic Accessibility Using Neurosnap

Neurosnap has tools which can help estimate or predict synthetic accessibility. Here’s how to use them, what they provide, and how to interpret their outputs.

Neurosnap’s Tools Related to SA

1. eTox (Drug Toxicity Prediction Service)

   • Predicts two relevant metrics: toxicity probability (0–1) and synthetic accessibility (from 1 to 10). In this scale, 1 = easy to make, 10 = very difficult to synthesize.
   • So, via eTox you can directly obtain a predicted SA score in the familiar Ertl & Schuffenhauer‐style scale. This lets you judge whether a molecule is likely to be feasible or too challenging.

2. Mordred (Molecular Descriptor Calculator)

   • Does not directly compute SA. Instead, it produces ~1,614 molecular descriptors (constitutional, topological, geometrical, charge, etc.). Some of those descriptors correlate with synthetic difficulty.

   • Examples of descriptors Mordred produces that are indirectly informative of synthetic accessibility include:

     • Size & Complexity: number of atoms (heavy atoms), molecular weight, Bertz complexity index (BertzCT), etc.
     • Functional groups: counts of heteroatoms (e.g., N, O), counts of double/triple/aromatic bonds, acids/bases.
     • Structural features: counts of spiro atoms, bridgeheads; ratio of hybridizations; ring descriptors.
     • Graph complexity: measures of branching (Zagreb indices, Wiener path number, etc.).

   • Because Mordred does not output a SA score, you can use its descriptors to build or feed into a predictive model (if you or your team have one) or use them heuristically to flag molecules that are likely to be difficult.

Interpreting SA with Neurosnap

• If using eTox, lower SA score (closer to 1) is good / easier. Higher (closer to 10) means harder. Balance this alongside predicted activity, toxicity, ADMET etc.

• If using Mordred, you’ll often look for “red flags” in descriptors:

• You can combine several Mordred descriptors into a heuristic scoring or even train a simple regression model (if you have known SA scores for some compounds) to approximate SA for new compounds.

A Suggested Workflow Using Neurosnap to Assess Synthetic Accessibility

Here’s a suggested workflow for someone designing or evaluating small molecules using Neurosnap:

1. Generate or gather candidate molecules you are interested in (SMILES, structure files etc.).

2. Run eTox on these molecules:

   • Get toxicity prediction (0‑1)
   • Get SA prediction (1‑10)
   • Flag molecules with high toxicity or high SA score (i.e. difficult to synthesize) for further scrutiny or perhaps discard from early‐stage prioritization.

3. Run Mordred descriptor calculation in parallel (or for all candidates / for those flagged by eTox) to get the set of molecular descriptors.

4. Analyze Mordred descriptors for synthetic complexity indicators as above (e.g. high BertzCT, many bridgehead/spiro atoms, complex ring systems, etc.).

5. Optionally, compute RDKit’s SA_Score (if you have access / pipeline for that) to compare or calibrate with eTox’s SA output.

6. Rank/prioritize molecules combining synthetic accessibility with other important metrics: potency, predicted toxicity, solubility, cost, novelty, etc. Use a multi‐objective metric or filtering strategy.

7. Iterate & refine: if many molecules are flagged as hard (SA high), consider modifying molecular design: remove / simplify rare fragments / reduce functional group count / avoid complex ring systems / reduce stereochemical complexity.

Advantages & Pitfalls

• Advantages:

  • Saves time and resources by avoiding molecules that are improbable to synthesize.
  • Helps filter and prioritize in silico designs.
  • Enables trade‐offs between potency and manufacturability.
  • Enhances alignment between computational design and laboratory feasibility.

• Pitfalls / Limitations:

  • SA predictions are approximations; they do not guarantee a viable synthetic route.
  • Some “hard” molecules may actually be synthesizable with advanced methods, or by using specialist chemistry, or by collaborating with high‐expert labs.
  • Descriptor‐based models (including SA score heuristics) often do not capture cost of starting materials, availability of reagents, scale, or yields.
  • New reaction methodology developments may make previously hard molecules easier; models may lag behind new synthetic advances.

Conclusion

Synthetic Accessibility is a critical lens through which to evaluate small molecules in drug discovery and small‐molecule biology. It integrates complexity, synthetic risk, and practical feasibility, and is essential for realistic prioritization of candidates.

With Neurosnap’s tools (eTox for direct SA scoring, Mordred for descriptor‐based proxies), teams can efficiently assess SA early in the design process. By combining those tools in a thoughtful workflow, one can reduce wasted effort, improve the realism of proposed molecules, and accelerate progress toward molecules that both work biologically and can actually be made.

References & Further Reading

• Ertl, P.; Schuffenhauer, A. “Estimation of Synthetic Accessibility Score of Drug‐like Molecules based on Molecular Complexity and Fragment Contributions.” Journal of Cheminformatics 2009. (BioMed Central)
• RDKit documentation & source code: SA_Score (Ertl & Schuffenhauer implementation) (GitHub)
• “Integrating Synthetic Accessibility with AI‐Based Generative Drug Design,” J. Cheminformatics. (BioMed Central)
• Articles on descriptor‐based SA estimation and structure vs synthetic feasibility trade‐offs (ScienceDirect)