Detail

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

  1. Connor W. Coley
  2. Dale A. Thomas III 
  3. Justin A. M. Lummiss
  4. Jonathan N. Jaworski 
  5. Christopher P. Breen
  6. Victor Schultz
  7. Travis Hart
  8. Joshua S. Fishman
  9. Luke Rogers
  10. Hanyu Gao1 
  11. Robert W. Hicklin 
  12. Pieter P. Plehiers
  13. Joshua Byington
  14. John S. Piotti
  15. William H. Green 
  16. A. John Hart
  17. Timothy F. Jamison
  18. Klavs F. Jensen
  1. 1Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.
  2. 2Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.
  3. 3Department of Chemistry, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.

 

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.

Structured Abstract

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.

 

Click https://science.sciencemag.org/content/365/6453/eaax1566.full here to read the full article. 

Source: 

Science  09 Aug 2019:
Vol. 365, Issue 6453, eaax1566
DOI: 10.1126/science.aax1566

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