首页|Findings from Massachusetts Institute of Technology Provide New Insights into Ma chine Learning (Calibration-free Reaction Yield Quantification By Hplc With a Ma chine-learning Model of Extinction Coefficients)
Findings from Massachusetts Institute of Technology Provide New Insights into Ma chine Learning (Calibration-free Reaction Yield Quantification By Hplc With a Ma chine-learning Model of Extinction Coefficients)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Machine Learning is now available. According to news originating from Cambridge, Massachusetts, by News Rx correspondents, research stated, "Reaction optimization and characterization depend on reliable measures of reaction yield, often measured by high-performanc e liquid chromatography (HPLC). Peak areas in HPLC chromatograms are correlated to analyte concentrations by way of calibration standards, typically pure sample s of known concentration." Funders for this research include Defense Advanced Research Projects Agency (DAR PA), DARPA Accelerated Molecular Discovery, MIT Machine Learning for Pharmaceuti cal Discovery and Synthesis (MLPDS) consortium. Our news journalists obtained a quote from the research from the Massachusetts I nstitute of Technology, "Preparing the pure material required for calibration ru ns can be tedious for low-yielding reactions and technically challenging at smal l reaction scales. Herein, we present a method to quantify the yield of reaction s by HPLC without needing to isolate the product(s) by combining a machine learn ing model for molar extinction coefficient estimation, and both UV-vis absorptio n and mass spectra. We demonstrate the method for a variety of reactions importa nt in medicinal and process chemistry, including amide couplings, palladium cata lyzed cross-couplings, nucleophilic aromatic substitutions, aminations, and hete rocycle syntheses. The reactions were all performed using an automated synthesis and isolation platform. Calibration-free methods such as the presented approach are necessary for such automated platforms to be able to discover, characterize , and optimize reactions automatically."
CambridgeMassachusettsUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningMa ssachusetts Institute of Technology
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