Predicting All Outcomes
A machine learning algorithm aims to take the “what ifs” out of chemical synthesis
Maryam Mahdi | | Quick Read
Synthetic chemists who want to conduct novel reactions typically turn to the results of previous experiments to help ascertain the optimal conditions for enantioselectivity – and the likelihood of success. But it’s a time-consuming and error-prone process. Matthew Sigman, a chemist at the University of Utah, estimates that there can be anywhere between seven and ten variables in a typical pharmaceutical reaction. “With billions of possibilities, you cannot cover all the variable space with any type of high throughput operation,” he says.
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