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Machine learning model helps characterize compounds for drug discovery

WEST LAFAYETTE, Ind. – Tandem mass spectrometry is a powerful analytical tool used to characterize complex mixtures in drug discovery and other fields.

Now, Purdue University innovators have created a new method of applying machine learning concepts to the tandem mass spectrometry process to improve the flow of information in the development of new drugs. Their work is published in Chemical Science.

“Mass spectrometry plays an integral role in drug discovery and development,” said Gaurav Chopra, an assistant professor of analytical and physical chemistry in Purdue’s College of Science. “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 characterization of compounds by chemists.”

Chopra said there are two major problems in the field of machine learning used for chemical sciences. Methods used do not provide chemical understanding

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Researchers create p16-Cre ERT2 -tdTomato mouse model to characterize in vivo senescent cells

Cell senescence is a state of permanent cell cycle arrest that was initially defined for cells grown in cell culture. It plays a key role in age-associated organ dysfunction and age-related diseases such as cancer, but the in vivo pathogenesis is largely unclear.

A research team led by Professor Makoto Nakanishi of the Institute of Medical Science, the University of Tokyo, generated a p16-Cre ERT2 -tdTomato mouse model to characterize in vivo p16 high cells at the single-cell level.

They found tdTomato-positive p16 high cells detectable in all organs, which were enriched with age. They also found that these cells failed to proliferate and had half-lives ranging from 2.6 to 4.2 months, depending on the tissue examined.

Single-cell transcriptomics in the liver and kidneys revealed that p16 high cells were present in various cell types, though most dominant in hepatic endothelium and

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