AI Powered Robotic Platform Automates Molecule Production

A system developed by MIT researchers which is lead by artificial intelligence and powered by a robotic platform,  moves a step closer to automating the production of small molecules that could be used in medicine, solar energy, and polymer chemistry.

The system, described in the August 8 issue of Science, could free up bench chemists from a variety of routine and time-consuming tasks, and may suggest possibilities for how to make new molecular compounds, according to the study co-leaders Klavs F. Jensen, the Warren K. Lewis Professor of Chemical Engineering, and Timothy F. Jamison, the Robert R. Taylor Professor of Chemistry and associate provost at MIT.

The technology “has the promise to help people cut out all the tedious parts of molecule building,” including looking up potential reaction pathways and building the components of a molecular assembly line each time a new molecule is produced, says Jensen.

“And as a chemist, it may give you inspirations for new reactions that you hadn’t thought about before,” he adds.

Other MIT authors on the Science paper include Connor W. Coley, Dale A. Thomas III, Justin A. M. Lummiss, Jonathan N. Jaworski, Christopher P. Breen, Victor Schultz, Travis Hart, Joshua S. Fishman, Luke Rogers, Hanyu Gao, Robert W. Hicklin, Pieter P. Plehiers, Joshua Byington, John S. Piotti, William H. Green, and A. John Hart.

From inspiration to recipe to finished product

The new system combines three main steps. First, software guided by artificial intelligence suggests a route for synthesizing a molecule, then expert chemists review this route and refine it into a chemical “recipe,” and finally the recipe is sent to a robotic platform that automatically assembles the hardware and performs the reactions that build the molecule.

Coley and his colleagues have been working for more than three years to develop the open-source software suite that suggests and prioritizes possible synthesis routes. At the heart of the software are several neural network models, which the researchers trained on millions of previously published chemical reactions drawn from the Reaxys and U.S. Patent and Trademark Office databases. The software uses these data to identify the reaction transformations and conditions that it believes will be suitable for building a new compound.

Chemists then review the suggested synthesis routes produced by the software to build a more complete recipe for the target molecule. The chemists sometimes need to perform lab experiments or tinker with reagent concentrations and reaction temperatures, among other changes.

Unlike the continuous flow system the researchers presented last year, which had to be manually configured after each synthesis, the new system is entirely configured by the robotic platform.

The design for the platform, which is about two cubic meters in size — slightly smaller than a standard chemical fume hood — resembles a telephone switchboard and operator system that moves connections between the modules on the platform.

Toward full automation

The researchers tested the full system by creating 15 different medicinal small molecules of different synthesis complexity, with processes taking anywhere between two hours for the simplest creations to about 68 hours for manufacturing multiple compounds.

The team synthesized a variety of compounds: aspirin and the antibiotic secnidazole in back-to-back processes; the painkiller lidocaine and the antianxiety drug diazepam in back-to-back processes using a common feedstock of reagents; the blood thinner warfarin and the Parkinson’s disease drug safinamide, to show how the software could design compounds with similar molecular components but differing 3-D structures; and a family of five ACE inhibitor drugs and a family of four nonsteroidal anti-inflammatory drugs.

The research was supported, in part, by the U.S. Defense Advanced Research Projects Agency (DARPA) Make-It program.

Source: scitechdaily

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