A robotic platform for flow synthesis of organic compounds informed by AI planning

By Connor W. Coley, D. Thomas, Justin A M Lummiss, Jonathan N. Jaworski, Christopher P Breen, Victor Schultz, Travis Hart, Joshua S. Fishman, Luke Rogers, Hanyu Gao and 10 more. Science, 2019

847 citations recorded by the source corpus.

Pairing prediction and robotic synthesis Progress in automated synthesis of organic compounds has been proceeding along parallel tracks. One goal is algorithmic prediction of viable routes to a desired compound; the other is implementation of a known reaction sequence on a platform that needs little to no human intervention. Coley et al. now report preliminary integration of these two protocols. They paired a retrosynthesis prediction algorithm with a robotically reconfigurable flow apparatus. Human intervention was still required to supplement the predictor with practical considerations such as solvent choice and precise stoichiometry, although predictions should improve as accessible data accumulate for training. Science, this issue p. eaax1566 An automated synthesis platform conducts reactions on the basis of a human-devised workflow informed by a retrosynthesis algorithm. INTRODUCTION The ability to synthesize complex organic molecules is essential to the discovery and manufacture of functional compounds, including small-molecule medicines. Despite advances in laboratory automation, the identification and development of synthetic routes remain a manual process and experimental synthesis platforms must be manually configured to suit the type of chemistry to be performed, requiring time and effort investment from expert chemists. The ideal automated synthesis platform would be capable of planning its own synthetic routes and executing them under conditions that facilitate scale-up to production goals. Individual elements of the chemical development process (design, route development, experimental configuration, and execution) have been streamlined in previous studies, but none has presented a path toward integration of computer-aided synthesis planning (CASP), expert refined chemical recipe generation, and robotically executed chemical synthesis. RATIONALE We describe an approach toward automated, scalable synthesis that combines techniques in artificial intelligence (AI) for planning and robotics for execution. Millions of previously published reactions inform the computational design of synthetic routes; expert-refined chemical recipe files (CRFs) are run on a robotic flow chemistry platform for scalable, reproducible synthesis. This development strategy augments a chemist’s ability to approach target-oriented flow synthesis while substantially reducing the necessary information gathering and manual effort. RESULTS We developed an open source software suite for CASP trained on millions of reactions from the Reaxys database and the U.S. Patent and Trademark Office. The software was designed to generalize known chemical reactions to new substrates by learning to apply retrosynthetic transformations, to identify suitable reaction conditions, and to evaluate whether reactions are likely to be successful when attempted experimentally. Suggested routes partially populate CRFs, which require additional details from chemist users to define residence times, stoichiometries, and concentrations that are compatible with continuous flow. To execute these syntheses, a robotic arm assembles modular process units (reactors and separators) into a continuous flow path according to the desired process configuration defined in the CRF. The robot also connects reagent lines and computer-controlled pumps to reactor inlets through a fluidic switchboard. When that is completed, the system primes the lines and starts the synthesis. After a specified synthesis time, the system flushes the lines with a cleaning solvent, and the robotic arm disconnects reagent lines and removes process modules to their appropriate storage locations. This paradigm of flow chemistry development was demonstrated for a suite of 15 medicinally relevant small molecules. In order of increasing complexity, we investigated the synthesis of aspirin and secnidazole run back to back; lidocaine and diazepam run back to back to use a common feedstock; (S)-warfarin and safinamide to demonstrate the planning program’s stereochemical awareness; and two compound libraries: a family of five ACE inhibitors including quinapril and a family of four nonsteroidal anti-inflammatory drugs including celecoxib. These targets required a total of eight particular retrosynthetic routes and nine specific process configurations. CONCLUSION The software and platform herein represent a milestone on the path toward fully autonomous chemical synthesis, where routes still require human input and process development. Over time, the results generated by this and similar automated experimental platforms may reduce our reliance on historical reaction data, particularly in combination with smaller-scale flow-screening platforms. Increased availability of reaction data will further enable robotically realized syntheses based on AI recommendations, relieving expert chemists of manual tasks so that they may focus on new ideas. Planning and execution. A robotically reconfigurable flow chemistry platform performs multistep chemical syntheses planned in part by AI. The synthesis of complex organic molecules requires several stages, from ideation to execution, that require time and effort investment from expert chemists. Here, we report a step toward a paradigm of chemical synthesis that relieves chemists from routine tasks, combining artificial intelligence–driven synthesis planning and a robotically controlled experimental platform. Synthetic routes are proposed through generalization of millions of published chemical reactions and validated in silico to maximize their likelihood of success. Additional implementation details are determined by expert chemists and recorded in reusable recipe files, which are executed by a modular continuous-flow platform that is automatically reconfigured by a robotic arm to set up the required unit operations and carry out the reaction. This strategy for computer-augmented chemical synthesis is demonstrated for 15 drug or drug-like substances.

Subfields: Biomedical Engineering, Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/78681942efaa59e2aa92d0ebfdb8539e4f2df95b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

In Materials Chemistry

In Molecular Biology

Citations in the corpus

Artificial intelligence in drug development (2025, positive): “…planning process commences with retrosynthetic analysis that breaks down target molecules into commercially available or known building blocks, followed by reaction prediction to anticipate the required chemical reactions and conditions for synthesizing the target molecule. b , Automated synthesis.”
An autonomous laboratory for the accelerated synthesis of inorganic materials (2023, positive): “When these precursors do not cover all the elements in the target, we use a masked precursor completion model 12 to account for such missing precursors.”
Machine learning for a sustainable energy future (2022, methodology): “…to plan and perform experiments, including the automation of retrosynthesis [G] analysis (such as in reinforcement learning aided synthesis planning 124,169 ), prediction of reaction products (such as in convolutional neural networks [G] (CNNs) for reaction prediction 137,138 ) and reaction…”
Empowering Biomedical Discovery with AI Agents (2024, neutral): “To manage tasks from diverse environments, agents require tools to boost their capabilities [138].”
Augmenting large language models with chemistry tools (2023, background): “For instance, synthetic planning capabilities can benefit from an improved underlying synthesis engine, an active area of research 23,64,65 .”
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development (2021, methodology): “Machine learning model and automate and accelerate the process, understand the catalytic mechanism, and providing an insight into novel catalytic design (Zahrt et al. 2019, Coley et al. 2019).”
Autonomous chemical research with large language models (2023, positive): “Examples range from the autonomous discovery 17,18 and optimization of organic reactions 19 to the development of automated flow systems 20,21 and mobile platforms 22 .”
Emerging materials intelligence ecosystems propelled by machine learning (2020, methodology): “Likewise, materials synthesis and characterization facilities are beginning to see dramatic improvements in efficiencies due to the integration of ML capabilities (which provide autonomy) and the incorporation of robotic control (which imparts automation) 5 .”