Researchers from Purdue University, USA, have combined machine learning and tandem mass spectrometry to improve the flow of information in drug development.
Tandem mass spectrometry is a powerful tool used to characterise compounds in drug development. As published in Chemical Science, the team outlined two major problems in the use of machine learning in chemical sciences. The methods that are currently used do not provide a chemical understanding of the decisions that are made by the algorithm, and new methods are not typically used in blind experimental tests to see if the proposed models are accurate for use in a chemical laboratory.
Gaurav Chopra, an assistant professor of analytical and physical chemistry in Purdue’s College of Science, said: “Mass spectrometry plays an integral role in drug discovery and development. The specific implementation of bootstrapped machine learning with a small amount of positive and negative training data presented here will pave the way for becoming mainstream in day-to-day activities of automating characterisation of compounds by chemists.
“We have addressed both of these items for a methodology that is isomer selective and extremely useful in chemical sciences to characterise complex mixtures, identify chemical reactions and drug metabolites, and in fields such as proteomics and metabolomics.”
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