Controlling an organic synthesis robot with machine learning to search for new reactivity

By J. Granda, Liva Donina, Vincenza Dragone, D. Long, L. Cronin. Nature, 2018

579 citations recorded by the source corpus.

The discovery of chemical reactions is an inherently unpredictable and time-consuming process1. An attractive alternative is to predict reactivity, although relevant approaches, such as computer-aided reaction design, are still in their infancy2. Reaction prediction based on high-level quantum chemical methods is complex3, even for simple molecules. Although machine learning is powerful for data analysis4,5, its applications in chemistry are still being developed6. Inspired by strategies based on chemists’ intuition7, we propose that a reaction system controlled by a machine learning algorithm may be able to explore the space of chemical reactions quickly, especially if trained by an expert8. Here we present an organic synthesis robot that can perform chemical reactions and analysis faster than they can be performed manually, as well as predict the reactivity of possible reagent combinations after conducting a small number of experiments, thus effectively navigating chemical reaction space. By using machine learning for decision making, enabled by binary encoding of the chemical inputs, the reactions can be assessed in real time using nuclear magnetic resonance and infrared spectroscopy. The machine learning system was able to predict the reactivity of about 1,000 reaction combinations with accuracy greater than 80 per cent after considering the outcomes of slightly over 10 per cent of the dataset. This approach was also used to calculate the reactivity of published datasets. Further, by using real-time data from our robot, these predictions were followed up manually by a chemist, leading to the discovery of four reactions. A robot instructed by a machine learning algorithm and coupled with real-time spectroscopic systems provides fast and accurate reaction outcome predictions and reactivity assessments, leading to the discovery of new reactions.

Subfields: Biomedical Engineering, Materials Chemistry, Physical and Theoretical Chemistry

View Paper: https://www.semanticscholar.org/paper/a450156bd8a1e3b808f93e959ae9e79983c5751b

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Citations in the corpus

Machine learning for a sustainable energy future (2022, background): “…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 condition optimization (such as in robotic workflows [G] optimized by active learning [G] 19,170–174 ).”
Autonomous chemical research with large language models (2023, unknown): “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 .”
Closed-loop optimization of fast-charging protocols for batteries with machine learning (2020, methodology): “Adaptive OED algorithms have been successfully applied to physical science domains, such as materials science 1,2,12–14 , chemistry 15,16 , biology 17 and drug discovery 18 , as well as to computer science domains, such as hyperparameter optimization for machine learning 19,20 .”
Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare (2022, methodology): “AI aided immunohistochemical method to detect the multi-tumor, which overcomes the sensitivity limitation of the manual immunohistochemical method.”