Using machine learning in drug discovery is no longer a novel procedure and has been well documented in recent literature. Now, researchers are using machine learning in unique ways. For one, they are building a chemical library that helps researchers understand reaction outcomes during drug development.
"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."
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Findings were published in Organic Letters.
"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.
"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. "We believe that working hand-to-hand with organic and computational chemists will open up a new avenue for solving complex chemical reactivity problems for other reactions 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. "We believe that these models have the ability not only be used to predict reactions but also be used to better understand when a given reaction will occur. To demonstrate this, we used our model to guide additional substrates to test whether a reaction will occur."