Unlocking the Potential of Automated Organic Synthesis with Software

Organic synthesis is a powerful tool for producing complex molecules, and its automation has revolutionised the field by paving way for faster, more efficient organic compound synthesis.

Increased productivity, lower costs, and improved safety are just a few advantages of automated organic synthesis. But it’s not that simple; Automation of organic synthesis requires specialised tools and knowledge to operate, where specialised software becomes a pivotal interface between the chemist and the machine.

Some regard such software as merely a reaction submission system. The truth is, its capabilities extend far beyond that. This type of software can be used to design and optimise synthetic pathways, predict reaction outcomes, and generate detailed reports on the results. It can also be used to simulate reaction conditions, analyze reaction data, and maximize process efficiency. Ultimately, it can facilitate data-driven decision-making to save time and money by streamlining the process of organic synthesis.

High-level Feature Architecture

To build an ideal automated organic synthesis management platform, the software should have the following modules:

• Computer assisted synthesis planning (CASP)
• Reaction submission system
• Inventory management
• Integration with external databases and ability to interface with ELNs
• Instrument Control Software
• Data Acquisition and Analysis Software
• Database Management Software
• Visualisation Software
• Additional Specialised Software Packages

Software Capabilities

The high-level workflow of automated synthesis software, should address two major issues: Firstly, what reactions should be realised, and secondly, how precisely they should be performed.

The first stage is referred to as “computer-assisted synthesis planning.” Once the desired strategy is established, it’s time for the synthesis management part to translate the chosen reactions into physical operations with reactants, solvents, chemical hardware, and analytical instruments.

Target assessment

Once users have identified the desired products, the system may serve as a wise advisor. An ideal software will provide the ability to assess the synthetic feasibility for a particularly configured robotic platform, after which the novelty assessment module could kick in to ensure patent purity. To aid decision making, drug-likeness, regulatory constraints, and more advanced properties of interest can all be easily incorporated into the workflow. Modularity is key.

Smart retrosynthesis logic

The integration of retrosynthetic analysis module allows for the prediction of viable synthetic routes to a desired product. Synthetic routes must be further evaluated and ranked based on their automation compatibility, taking into account the specific hardware platform available at hand, and not just their chemical feasibility. These steps have yet to be addressed in current and future solutions.

Convenient reaction submission interface

An ergonomic, user-friendly, and efficient interface is indispensable. It’s what chemists face the most when working with the system. A quick learning curve would not only speed up reaction submission but would also foster compliance and comfort. Chemistry is a creative process, and At HMND we firmly believe that building software that inspires is crucial, especially in the domains of research where scientific innovation can be catalysed by a convenient user experience.

Recommendation of reaction conditions

Retrosynthesis is an important step in autonomous organic synthesis, but it does not address various practical considerations involved in the process. In order to actually execute a synthetic route, precise specifications like amounts of each reactant, order of addition, solvent, temperature, and time must be taken into account. These specifics are frequently missing from databases and other data-driven tools, resulting in outcomes that are significantly different than expected.

Reaction abstraction

Under the hood of the reaction submission interface resides the essential layer that translates chemical scheme into unified description of hardware operations, i.e. what, when and how actual physical operations should be performed. These of course are platform-specific.

Reaction templates library

Surely enough, an automated organic synthesis suite would greatly benefit from a list of available and ready-made synthetic methods. Currently, they tend to be limited by particular platform capabilities, albeit steps towards global accessibility are being undertaken. Namely, the Open Reaction Database is available for open access to support machine learning, reaction prediction, chemical synthesis planning, and experiment design. ChemPuter illustrates another fascinating approach: imagine being able to download your protocol from a public repository and run it seamlessly on your hardware!

Reaction prediction

The chemical space is ever expanding, and automated synthesis can be considered a locomotive of the process. It means that robotic systems will routinely perform reactions between previously unknown combinations of reagents with uncertain results. To improve the success rates and value of the whole system predictive modelling of reaction outcomes would be invaluable, as it would dramatically decrease the number of unsuccessful reaction runs while increasing the productive throughput of the robotic setup and decreasing the consumption of potentially costly reactants.

Error handling

The use of platforms that can adaptively adjust their action sequences through trial and error can reduce the need for meticulous planning, allowing for more flexibility in organic synthesis.

Predictive structure elucidation

Even low-level adaptive optimisation necessitates automated confirmation of the product identity. To avoid the need for user-provided standards (which likely are unavailable anyway) advanced predictive analytical software should be designed.

Self-learning

An ideal platform for organic synthesis would possess the same qualities as a seasoned chemist: the ability to learn, adjust, and improve over time, as well as the capacity to respond to unexpected outcomes. By leveraging these autonomous capabilities, the platform would be able to bypass the need for meticulous planning and provide a more flexible approach to organic synthesis.

Data deposition to realise a collective body of chemical knowledge

This might be a game changer in the field of organic synthesis. By leveraging the vast amount of information available, scientists and researchers can uncover new pathways and strategies for synthesising complex molecules. This could lead to more efficient and cost-effective methods of producing organic compounds, which could drastically improve the way we approach drug discovery and development like the Internet changed the way we share and access the information.

The integration of new data into predictive algorithms

presents a unique challenge, as the amount of data generated by a single platform is likely to be dwarfed by the historical reactions stored in existing databases. Furthermore, the type of data generated is also qualitatively different, as it has the potential to be far more detailed in terms of procedural details and analytical chemistry; however, it will likely be unable to match the diversity of substrates found in published reactions due to the size of the chemical inventory. As such, a new challenge arises in algorithm design: how to effectively leverage this multi-modal data. If the database is further enhanced by the outcomes of reaction testing conducted within the same platform, quantitative evaluations — both successful and unsuccessful — can be used to hone the algorithm.

Challenging features impeding breakthrough

• The design of user-friendly interfaces which allow scientists to easily input data into programs and interpret results from experiments quickly.
• Devising a smart synthesis planning module to aid in uncovering novel and feasible paths with pattern analysis.
• Representing reactions accurately and efficiently so that robots can understand the information encoded by them.
• Developing algorithms to predict reaction outcomes with high fidelity, as well as to automate characterisation of the obtained products.
• Collecting and integrating multi-modal data to enable feedback loop and self-learning

Outlook

The challenges envisioned for data-driven organic synthesis software include flawless integration with hardware, but the ultimate desiderata are smart synthesis planning, adaptiveness, error handling, and self-learning. Data availability is a particular impediment, while transitioning from automation to autonomy implies a certain degree of adaptiveness that is difficult to achieve with the limited analytical capabilities of many platforms. Leveraging multi-modality in algorithm design is also a new challenge.

Computers have now enabled predictive reaction calculations to be performed with increasing sophistication, as well as the automation of complex molecules through retrosynthesis. Computational power and advances in hardware and software can pave the way for an exciting future where molecules can be automatically designed, synthesiсed, and tested more efficiently than ever before.

AI and ML will play a critical role on the lab bench of the future, requiring tidy data collection. Reaction templates enable the creation of a structured network of discrete paths for reproducible chemical synthesis. Machine learning techniques can analyse these networks to discover new, viable paths. Annotating reaction networks with screening results can refine algorithms and reduce operation costs by identifying intermediates that need to be synthesized in larger quantities. AI-based technology can facilitate the navigation of chemical transformation space more efficiently and lead to the reshaping of this space via discovery of “synthetic wormholes”.

Future software should become a research assistant that can help scientists design more efficient methods for synthesising chemicals, which could lead to faster production times and lower costs associated with manufacturing drugs or other products derived from organic chemistry processes. Additionally, it may also enable better prediction of reaction outcomes so that fewer experiments need to be conducted before a successful result is achieved.

The development of a fully integrated, globally accessible and remotely controlled, automated chemical synthesis laboratory is an exciting opportunity to revolutionise the way we approach chemical synthesis. With the right provisional software design, this laboratory could be a powerful tool for researchers around the world to access and utilise in their work.

At HMND we see that by creating a system that is both accessible and automated, we can make chemical synthesis more efficient and cost-effective than ever before. This is an exciting project that has the potential to make a real impact on the field of chemistry.