Machine learning

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Purdue University innovators have introduced chemical reactivity flowcharts to help chemists interpret reaction outcomes using statistically robust machine learning models trained on a small number of reactions.

WEST LAFAYETTE — Machine learning has been used widely in the chemical sciences for drug design and other processes.

The models that are prospectively tested for new reaction outcomes and used to enhance human understanding to interpret chemical reactivity decisions made by such models are extremely limited.

Purdue University innovators have introduced chemical reactivity flowcharts to help chemists interpret reaction outcomes using statistically robust machine learning models trained on a small number of reactions. The work is published in Organic Letters.

“Developing new and fast reactions is essential for chemical library design in drug discovery,” said Gaurav Chopra, an assistant professor of analytical and physical chemistry in Purdue’s College of Science. “We have developed a new, fast and one-pot multicomponent reaction (MCR) of N-sulfonylimines that was used as a representative case for generating training data for machine learning models, predicting reaction outcomes and testing new reactions in a blind prospective manner.

“We expect this work to pave the way in changing the current paradigm by developing accurate, human understandable machine learning models to interpret reaction outcomes that will augment the creativity and efficiency of human chemists to discover new chemical reactions and enhance organic and process chemistry pipelines.”

Chopra said the Purdue team’s human-interpretable machine learning approach, introduced as chemical reactivity flowcharts, can be extended to explore the reactivity of any MCR or any chemical reaction. It does not need large-scale robotics since these methods can be used by the chemists while doing reaction screening in their laboratories.

“We provide the first report of a framework to combine fast synthetic chemistry experiments and quantum chemical calculations for understanding reaction mechanism and human-interpretable statistically robust machine learning models to identify chemical patterns for predicting and experimentally testing heterogeneous reactivity of N-sulfonylimines,” Chopra said.

This work aligns with other innovations and research from Chopra’s labs, whose team members work with the Purdue Research Foundation Office of Technology Commercialization to patent numerous technologies. To find out more information about their patented inventions, contact otcip@prf.org.

“The unprecedented use of a machine learning model in generating chemical reactivity flowcharts helped us to understand the reactivity of traditionally used different N-sulfonylimines in MCRs,” said Krupal Jethava, a postdoctoral fellow in Chopra’s laboratory, who co-authored the work.

Chopra said the Purdue researchers hope their work will pave the way to become one of many examples that will showcase the power of machine learning for new synthetic methodology development for drug design and beyond in the future.

“In this work, we strived to ensure that our machine learning model can be easily understood by chemists not well versed in this field,” said Jonathan Fine, a former Purdue graduate student, who co-authored the work.

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