Translating Retrosynthesis Software Outputs into Automated Synthesis Instructions

Retrosynthesis software such as SYNTHIA® can output detailed synthetic pathways to reach a target molecule. These outputs typically include reaction types, reagents, and literature references. While they are readily interpretable by chemists, they are not immediately executable by laboratory robots. Bridging this gap requires translating human-readable plans into machine-readable instructions—directives such as dispensing liquids, setting temperatures, or stirring for defined durations. Achieving this translation is non-trivial. Each step must be standardized with details including reagent amounts, order of addition, solvent choice, temperature, and reaction time. Today, chemists often perform this translation manually by writing protocols, but the field is moving toward direct software-to-hardware integration.

Standardizing Synthetic Recipes

One approach to narrowing this gap is the creation of standardized, machine-readable formats for chemical recipes. Researchers have even developed chemical programming languages that encode laboratory procedures in structured ways. For instance,  Synple chem is a benchtop automated synthesizer with a standardized reaction protocol for the most common reactions in organic chemistry (reductive animation, amide couplings, suzuki reactions, etc.), enabling chemists to simply load the corresponding reagent cartridge and their sample, then choose a preset program that determines time, temperature, and solvent volume. 

SYNTHIA elevates these efficiency gains further by designing routes for reactions that already have pre-established Synple chem programs/cartridges, enabling subsequent synthesis steps to be automated.

Integration with Laboratory Hardware

Retrosynthesis outputs must also be tailored to the capabilities of available automation platforms. Different automated synthesizers vary in their reagent handling capacity, supported reaction types, and environmental controls. Not every computationally proposed route can be executed on every machine. For example, a pathway requiring cryogenic metalation may not be feasible on a benchtop robotic reactor.

This is why automation compatibility has become an important scoring criterion. Researchers Gao and Coley have suggested that synthetic routes should be ranked not only by chemical feasibility but also by hardware compatibility. SYNTHIA represents this concept through its compatibility with the Synple automated synthesizer, which accelerates route design with a customized set of ranked potential pathways for a given target molecule. SYNTHIA can thus propose routes filtered through the Synple system, enabling users to select desired pathways and program the system to execute individual reaction steps.

Learn more about key strategies and approaches in retrosynthetic analysis here.

Challenges and Ongoing Developments

Despite significant progress, fully automated translation remains a work in progress. Every retrosynthetic step must be detailed into a protocol with operational conditions. Literature references often provide these, but extracting conditions automatically is complex. Advances in natural language processing (NLP) and text mining are beginning to automate the extraction of experimental details from publications, enabling AI models to generate executable procedures.

Another promising approach is the use of template-based protocols: libraries of standard operating procedures for common reaction types that can be populated with specific reagents and conditions. Data-centric integration further strengthens this process. By recording the outcome of every run, systems can learn which sets of instructions consistently produce reliable results, improving translation accuracy over time.

In summary, the process of converting retrosynthesis outputs into machine instructions involves refining routes for hardware compatibility, enriching them with conditions, encoding them in structured formats, and executing them via robotic platforms. With ongoing advances in AI, NLP, and automation, the gap between planning and execution is steadily closing. As autonomous laboratories evolve, retrosynthesis software outputs may increasingly flow directly into robotic execution, erasing the boundary between knowing what to do and carrying it out. 

References

1. Gao W, Raghavan P, Coley CW. Autonomous platforms for data-driven organic synthesis. Nat Commun. 2022 Feb 28;13(1):1075. doi: 10.1038/s41467-022-28736-4.
2. Chen, J.; Xu, Q. Artificial intelligence-driven autonomous laboratory for accelerating chemical discovery. Chem. Synth. 2025, 5, 76. https://dx.doi.org/10.20517/cs.2025.66